for Journals by Title or ISSN
for Articles by Keywords
help
Followed Journals
Journal you Follow: 0
 
Sign Up to follow journals, search in your chosen journals and, optionally, receive Email Alerts when new issues of your Followed Journals are published.
Already have an account? Sign In to see the journals you follow.
Journal Cover Swarm and Evolutionary Computation
  [SJR: 2.167]   [H-I: 22]   [2 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2210-6502
   Published by Elsevier Homepage  [3038 journals]
  • Node-depth phylogenetic-based encoding, a spanning-tree representation for
           evolutionary algorithms. part I: Proposal and properties analysis
    • Authors: Telma Woerle de Lima; Alexandre Cláudio Botazzo Delbem; Anderson da Silva Soares; Fernando Marques Federson; João Bosco Augusto London Junior; Jeffrey Van Baalen
      Pages: 1 - 10
      Abstract: Publication date: December 2016
      Source:Swarm and Evolutionary Computation, Volume 31
      Author(s): Telma Woerle de Lima, Alexandre Cláudio Botazzo Delbem, Anderson da Silva Soares, Fernando Marques Federson, João Bosco Augusto London Junior, Jeffrey Van Baalen
      Representation choice and the development of search operators are crucial aspects of the efficiency of Evolutionary Algorithms (EAs) in combinatorial problems. Several researchers have proposed representations and operators for EAs that manipulate spanning trees. This paper proposes a new encoding called Node-depth Phylogenetic-based Encoding (NPE). NPE represents spanning trees by the relation between nodes and their depths using a relatively simple codification/decodification process. The proposed NPE operators are based on methods used for tree rearrangement in phylogenetic tree reconstruction: subtree prune and regraft; and tree bisection and reconstruction. NPE and its operators are designed to have high locality, feasibility, low time complexity, be unbiased, and have independent weight. Therefore, NPE is a good choice of data structure for EAs applied to network design problems.

      PubDate: 2016-11-26T16:48:49Z
      DOI: 10.1016/j.swevo.2016.05.001
      Issue No: Vol. 31 (2016)
       
  • Maintaining regularity and generalization in data using the minimum
           description length principle and genetic algorithm: Case of grammatical
           inference
    • Authors: Hari Mohan Pandey; Ankit Chaudhary; Deepti Mehrotra; Graham Kendall
      Pages: 11 - 23
      Abstract: Publication date: December 2016
      Source:Swarm and Evolutionary Computation, Volume 31
      Author(s): Hari Mohan Pandey, Ankit Chaudhary, Deepti Mehrotra, Graham Kendall
      In this paper, a genetic algorithm with minimum description length (GAWMDL) is proposed for grammatical inference. The primary challenge of identifying a language of infinite cardinality from a finite set of examples should know when to generalize and specialize the training data. The minimum description length principle that has been incorporated addresses this issue is discussed in this paper. Previously, the e-GRIDS learning model was proposed, which enjoyed the merits of the minimum description length principle, but it is limited to positive examples only. The proposed GAWMDL, which incorporates a traditional genetic algorithm and has a powerful global exploration capability that can exploit an optimum offspring. This is an effective approach to handle a problem which has a large search space such the grammatical inference problem. The computational capability, the genetic algorithm poses is not questionable, but it still suffers from premature convergence mainly arising due to lack of population diversity. The proposed GAWMDL incorporates a bit mask oriented data structure that performs the reproduction operations, creating the mask, then Boolean based procedure is applied to create an offspring in a generative manner. The Boolean based procedure is capable of introducing diversity into the population, hence alleviating premature convergence. The proposed GAWMDL is applied in the context free as well as regular languages of varying complexities. The computational experiments show that the GAWMDL finds an optimal or close-to-optimal grammar. Two fold performance analysis have been performed. First, the GAWMDL has been evaluated against the elite mating pool genetic algorithm which was proposed to introduce diversity and to address premature convergence. GAWMDL is also tested against the improved tabular representation algorithm. In addition, the authors evaluate the performance of the GAWMDL against a genetic algorithm not using the minimum description length principle. Statistical tests demonstrate the superiority of the proposed algorithm. Overall, the proposed GAWMDL algorithm greatly improves the performance in three main aspects: maintains regularity of the data, alleviates premature convergence and is capable in grammatical inference from both positive and negative corpora.

      PubDate: 2016-11-26T16:48:49Z
      DOI: 10.1016/j.swevo.2016.05.002
      Issue No: Vol. 31 (2016)
       
  • Hybrid HSA and PSO algorithm for energy efficient cluster head selection
           in wireless sensor networks
    • Authors: T. Shankar; S. Shanmugavel; A. Rajesh
      Pages: 1 - 10
      Abstract: Publication date: October 2016
      Source:Swarm and Evolutionary Computation, Volume 30
      Author(s): T. Shankar, S. Shanmugavel, A. Rajesh
      Energy efficiency is a major concern in wireless sensor networks as the sensor nodes are battery-operated devices. For energy efficient data transmission, clustering based techniques are implemented through data aggregation so as to balance the energy consumption among the sensor nodes of the network. The existing clustering techniques make use of distinct Low-Energy Adaptive Clustering Hierarchy (LEACH), Harmony Search Algorithm (HSA) and Particle Swarm Optimization (PSO) algorithms. However, individually, these algorithms have exploration-exploitation tradeoff (PSO) and local search (HSA) constraint. In order to obtain a global search with faster convergence, a hybrid of HSA and PSO algorithm is proposed for energy efficient cluster head selection. The proposed algorithm exhibits high search efficiency of HSA and dynamic capability of PSO that improves the lifetime of sensor nodes. The performance of the hybrid algorithm is evaluated using the number of alive nodes, number of dead nodes, throughput and residual energy. The proposed hybrid HSA–PSO algorithm shows an improvement in residual energy and throughput by 83.89% and 29.00%, respectively, than the PSO algorithm.

      PubDate: 2016-09-25T09:40:37Z
      DOI: 10.1016/j.swevo.2016.03.003
      Issue No: Vol. 30 (2016)
       
  • Fuzzy evolutionary cellular learning automata model for text summarization
    • Authors: Razieh Abbasi-ghalehtaki; Hassan Khotanlou; Mansour Esmaeilpour
      Pages: 11 - 26
      Abstract: Publication date: October 2016
      Source:Swarm and Evolutionary Computation, Volume 30
      Author(s): Razieh Abbasi-ghalehtaki, Hassan Khotanlou, Mansour Esmaeilpour
      Text summarization is the automatic process of creating a short form of an original text. The main goal of an automatic text summarization system is production of a summary which satisfies the user's needs. In this paper, a new model for automatic text summarization is introduced which is based on fuzzy logic system, evolutionary algorithms and cellular learning automata. First, the most important features including word features, similarity measure, and the position and the length of a sentence are extracted. A linear combination of these features shows the importance of each sentence. To calculate similarity measure, a combined method based on artificial bee colony algorithm and cellular learning automata are used. In this method, joint n-grams among sentences are extracted by cellular learning automata and then an artificial bee colony algorithm classifies n-friends in order to extract data and optimize the similarity measure as fitness function. Moreover, a new approach is proposed to adjust the best weights of the text features using particle swarm optimization and genetic algorithm. This method discovers more important and less important text features and then assigns fair weights to them. At last, a fuzzy logic system is used to perform the final scoring. The results of the proposed approach were compared with the other methods including Msword, System19, System21, System28, System31, FSDH, FEOM, NetSum, CRF, SVM, DE, MA-SingleDocSum, Unified Rank and Manifold Ranking using ROUGE-l and ROUGE-2 measures on the DUC2002 dataset. The results show that proposed method outperforms the aforementioned methods.

      PubDate: 2016-09-25T09:40:37Z
      DOI: 10.1016/j.swevo.2016.03.004
      Issue No: Vol. 30 (2016)
       
  • An object tracking method using modified galaxy-based search algorithm
    • Authors: Faegheh Sardari; Mohsen Ebrahimi Moghaddam
      Pages: 27 - 38
      Abstract: Publication date: Available online 21 April 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Faegheh Sardari, Mohsen Ebrahimi Moghaddam
      Object tracking is a dynamic optimization process based on the temporal information related to the previous frames. Proposing a method with higher precision in complex environments is a challenge for researchers in this field of study. In this paper, we have proposed an object tracking method based on a meta-heuristic approach. Although there are some meta-heuristic approaches in the literature, we have modified GbSA (galaxy based search algorithm) which is more precise than related works. The GbSA searches the state space by simulating the movement of the spiral galaxy to find the optimum object state. The proposed method searches each frame of video with particle filter and the MGSbA in a similar manner. It receives current frame and the temporal information that is related to previous frames as input and tries to find the optimum object state in each one. The experimental results show the efficiency of this algorithm in comparison with results of related methods.

      PubDate: 2016-04-26T22:54:15Z
      DOI: 10.1016/j.swevo.2016.04.001
      Issue No: Vol. 30 (2016)
       
  • COOA: Competitive Optimization Algorithm
    • Authors: Yousef Sharafi; Mojtaba Ahmadieh Khanesar; Mohammad Teshnehlab
      Pages: 39 - 63
      Abstract: Publication date: Available online 18 April 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Yousef Sharafi, Mojtaba Ahmadieh Khanesar, Mohammad Teshnehlab
      This paper presents a novel optimization algorithm based on competitive behavior of various creatures such as birds, cats, bees and ants to survive in nature. In the proposed method, a competition is designed among all aforementioned creatures according to their performances. Every optimization algorithm can be appropriate for some objective functions and may not be appropriate for another. Due to the interaction between different optimization algorithms proposed in this paper, the algorithms acting based on the behavior of these creatures can compete each other for the best. The rules of competition between the optimization methods are based on imperialist competitive algorithm. Imperialist competitive algorithm decides which of the algorithms can survive and which of them must be extinct. In order to have a comparison to well-known heuristic global optimization methods, some simulations are carried out on some benchmark test functions with different and high dimensions. The obtained results shows that the proposed competition based optimization algorithm is an efficient method in finding the solution of optimization problems.

      PubDate: 2016-04-21T14:34:39Z
      DOI: 10.1016/j.swevo.2016.04.002
      Issue No: Vol. 30 (2016)
       
  • Using autonomous search for solving constraint satisfaction problems via
           new modern approaches
    • Authors: Ricardo Soto; Broderick Crawford; Rodrigo Olivares; Cristian Galleguillos; Carlos Castro; Franklin Johnson; Fernando Paredes; Enrique Norero
      Pages: 64 - 77
      Abstract: Publication date: Available online 21 April 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Ricardo Soto, Broderick Crawford, Rodrigo Olivares, Cristian Galleguillos, Carlos Castro, Franklin Johnson, Fernando Paredes, Enrique Norero
      Constraint Programming is a powerful paradigm which allows the resolution of many complex problems, such as scheduling, planning, and configuration. These problems are defined by a set of variables and a set of constraints. Each variable has non-empty domain of possible value and each constraint involves some subset of the variables and specifies the allowable combinations of values for that subset. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. There exist different ways to perform this selection, and depending on the quality of this decision, the efficiency of the solving process may dramatically vary. Autonomous search is a particular case of adaptive systems that aims at improving its solving performance by adapting itself to the problem at hand without manual configuration of an expert user. The goal is to improve their solving performance by modifying and adjusting themselves, either by self-adaptation or by supervised adaptation. This approach has been effectively applied to different optimization and satisfaction techniques such as constraint programming, metaheuristics, and SAT. In this paper, we present a new Autonomous Search approach for constraint programming based on four modern bio-inspired metaheuristics. The goal of those metaheuristics is to optimize the self-tuning phase of the constraint programming search process. We illustrate promising results, where the proposed approach is able to efficiently solve several well-known constraint satisfaction problems.

      PubDate: 2016-04-26T22:54:15Z
      DOI: 10.1016/j.swevo.2016.04.003
      Issue No: Vol. 30 (2016)
       
  • A Hierarchical Heterogeneous Ant Colony Optimization based approach for
           efficient Action Rule mining
    • Authors: N.K. Sreeja; A. Sankar
      Pages: 1 - 12
      Abstract: Publication date: Available online 5 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): N.K. Sreeja, A. Sankar
      Most data mining algorithms aim at discovering customer models and classification of customer profiles. Application of these data mining techniques to industrial problems such as customer relationship management helps in classification of customers with respect to their status. The mined information does not suggest any action that would result in reclassification of customer profile. Such actions would be useful to maximize the objective function, for instance, the net profit or minimizing the cost. These actions provide hints to a business user regarding the attributes that have to be changed to reclassify the customers from an undesirable class (Eg. disloyal) to the desired class (Eg. loyal). This paper proposes a novel algorithm called Hierarchical Heterogeneous Ant Colony Optimization based Action Rule Mining (HHACOARM) algorithm to generate action rules. The algorithm has been developed considering the resource constraints. The algorithm has ant agents at different levels in the hierarchy to identify the flexible attributes whose values need to be changed to mine action rules. The advantage of HHACOARM algorithm is that it generates optimal number of minimal cost action rules. HHACOARM algorithm does not generate invalid rules. Also, the computational complexity of HHACOARM algorithm is less compared to the existing action rule mining methods.

      PubDate: 2016-03-07T16:06:16Z
      DOI: 10.1016/j.swevo.2016.02.004
      Issue No: Vol. 29 (2016)
       
  • Multi-dimensional signaling method for population-based metaheuristics:
           Solving the large-scale scheduling problem in smart grids
    • Authors: João Soares; Mohammad Ali Fotouhi Ghazvini; Marco Silva; Zita Vale
      Pages: 13 - 32
      Abstract: Publication date: Available online 8 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): João Soares, M. Ali Fotouhi, Marco Silva, Zita Vale
      The dawn of smart grid is posing new challenges to grid operation. The introduction of Distributed Energy Resources (DER) requires tough planning and advanced tools to efficiently manage the system at reasonable costs. Virtual Power Players (VPP) are used as means of aggregating generation and demand, which enable smaller producers using different generation technologies to be more competitive. This paper discusses the problem of the centralized Energy Resource Management (ERM), including several types of resources, such as Demand Response (DR), Electric Vehicles (EV) and Energy Storage Systems (ESS) from the VPP׳s perspective to maximize profits. The complete formulation of this problem, which includes the network constraints, is represented with a complex large-scale mixed integer nonlinear problem. This paper focuses on deterministic and metaheuristics methods and proposes a new multi-dimensional signaling approach for population-based random search techniques. The new approach is tested with two networks with high penetration of DERs. The results show outstanding performance with the proposed multi-dimensional signaling and confirm that standard metaheuristics are prone to fail in solving these kind of problems.

      PubDate: 2016-03-12T16:17:45Z
      DOI: 10.1016/j.swevo.2016.02.005
      Issue No: Vol. 29 (2016)
       
  • Quantum Inspired Social Evolution (QSE) Algorithm for 0-1 Knapsack problem
    • Authors: R.S. Pavithr; Gursaran
      Pages: 33 - 46
      Abstract: Publication date: Available online 11 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): R.S. Pavithr, Gursaran
      Social Evolution (SE) algorithm [10] is inspired by human interactions and their bias. Generally, human bias influences with whom individuals interact and how they interact. The individual bias may also influence the outcome of interactions to be decisive or indecisive. When the interactions are decisive, the individuals may completely adopt the change. When the interactions are indecisive, individual may consult for a second opinion to further evaluate the indecisive interaction before adopting the change to emerge and evolve [10]. In the last decade, with the integration of emerging quantum computing with the traditional evolutional algorithms, quantum inspired evolutionary algorithm is evolved [2]. Inspired by Q-bit representation and parallelism and the success of the quantum inspired evolutionary algorithms, in this paper, a quantum inspired Social Evolution algorithm (QSE) is proposed by hybridizing Social evolution algorithm with the emerging quantum inspired evolutionary algorithm. The proposed QSE algorithm is studied on a well known 0-1 knapsack problem and the performance of the algorithm is compared with various evolutionary, swarm and quantum inspired evolutionary algorithm variants. The results indicate that, the performance of QSE algorithm is better than or comparable with the different evolutionary algorithmic variants tested with. An experimental study is also performed to investigate the impact and importance of human bias in selection of individuals for interactions, the rate of individuals seeking for second opinion and the influence of selective learning on the overall performance of Quantum inspired Social Evolution algorithm (QSE).

      PubDate: 2016-03-12T16:17:45Z
      DOI: 10.1016/j.swevo.2016.02.006
      Issue No: Vol. 29 (2016)
       
  • Hybrid self-adaptive Cuckoo search for global optimization
    • Authors: Uroš Mlakar; Iztok Fister; Iztok Fister
      Pages: 47 - 72
      Abstract: Publication date: Available online 29 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Uroš Mlakar, Iztok Fister, Iztok Fister
      Adaptation and hybridization typically improve the performances of original algorithm. This paper proposes a novel hybrid self-adaptive cuckoo search algorithm, which extends the original cuckoo search by adding three features, i.e., a balancing the exploration search strategies within the cuckoo search algorithm, a self-adaptation of cuckoo search control parameters and a linear population reduction. The algorithm was tested on 30 benchmark functions from the CEC-2014 test suite, giving promising results comparable to the algorithms, like the original differential evolution (DE) and original cuckoo search (CS), some powerful variants of modified cuckoo search (i.e., MOCS, CS-VSF) and self-adaptive differential evolution (i.e., jDE, SaDE), while overcoming the results of a winner of the CEC-2014 competition L-Shade remains a great challenge for the future.

      PubDate: 2016-04-01T15:30:25Z
      DOI: 10.1016/j.swevo.2016.03.001
      Issue No: Vol. 29 (2016)
       
  • Bio-inspired search algorithms for unstructured P2P overlay networks
    • Authors: Vesna Šešum-Čavić; Eva Kühn; Daniel Kanev
      Pages: 73 - 93
      Abstract: Publication date: Available online 4 April 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Vesna Šešum-Čavić, Eva Kühn, Daniel Kanev
      Efficient location and manipulation of complex and often incomplete data is a difficult, challenging task in nowadays extremely complex IT systems and on the Internet, overwhelmed with a huge amount of information. The problem itself is present in numerous different practical use-cases (e.g., in P2P streaming applications that rapidly gain more attention) and refers to the selection of the proper, efficient search algorithm. Research and commercial efforts resulted in a prolific offer of different algorithms that try to address this problem in the best possible way. Due to the huge complexity, intelligent algorithms are the most promising ones. However, everyday changing conditions impose finding even more advantageous approaches that will better cope with the problem, or at least address some “corner cases” better, than previously realized ones. In this paper, we propose a self-organizing approach inspired by bio-intelligence of slime molds that possesses distributive and autonomous properties with the goal to achieve a good query capability. A slime mold mechanism is adapted for search in an unstructured P2P system, and compared with Antnet and Gnutella search mechanisms. The benchmarks cover parameter sensitivity analysis, and comparative analysis. To validate the obtained results, a statistical analysis is performed. The obtained results show good scalability of slime mold algorithm and point to the selected “corner” cases where the slime mold algorithm has a total good performance (measured by different metrics).

      PubDate: 2016-04-06T15:57:56Z
      DOI: 10.1016/j.swevo.2016.03.002
      Issue No: Vol. 29 (2016)
       
  • Structural damage detection using artificial bee colony algorithm with
           hybrid search strategy
    • Authors: Z.H. Ding; M. Huang; Z.R. Lu
      Pages: 1 - 13
      Abstract: Publication date: Available online 7 January 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Z.H. Ding, M. Huang, Z.R. Lu
      An approach for structural damage detection using the artificial bee colony(ABC) algorithm with hybrid search strategy based on modal data is presented. More search strategies are offered and the bee will choose one search mode based on the tournament selection strategy. And a kind of elimination mechanism is introduced to improve the convergence rate. A truss and a plate are studied as two numerical examples to illustrate the efficiency of proposed method. An experimental work on a beam is studied for further verification. Final results show the present method can acquire the better identification results, compared with those from GA, the original ABC and quick ABC (QABC) algorithm, even under some measurement noise.

      PubDate: 2016-01-11T05:12:06Z
      DOI: 10.1016/j.swevo.2015.10.010
      Issue No: Vol. 28 (2016)
       
  • A hybridization of an Improved Particle Swarm optimization and
           Gravitational Search Algorithm for Multi-Robot Path Planning
    • Authors: P.K. Das; H.S. Behera; B.K. Panigrahi
      Pages: 14 - 28
      Abstract: Publication date: Available online 7 January 2016
      Source:Swarm and Evolutionary Computation
      Author(s): P.K Das, H.S. Behera, B.K. Panigrahi
      This paper proposed a new methodology to determine the optimal trajectory of the path for multi-robot in a clutter environment using hybridization of improved particle swarm optimization (IPSO) with an improved Gravitational Search algorithm (IGSA). The proposed approach embedded the social essence of IPSO with motion mechanism of IGSA. The proposed hybridization IPSO-IGSA maintain the efficient balance between exploration and exploitation because of adopting co-evolutionary techniques to update the IGSA acceleration and particle positions with IPSO velocity simultaneously. The objective of the algorithm is to minimize the maximum path length that corresponds to minimize the arrival time of all robots to their respective destination in the environment. The robot on the team make independent decisions, coordinate, and cooperate with each other to determine the next positions from their current position in the world map using proposed hybrid IPSO-IGSA. Finally the analytical and experimental results of the multi-robot path planning were compared to those obtained by IPSO-IGSA, IPSO, IGSA in a similar environment. The Simulation and the Khepera environment result show outperforms of IPSO-IGSA as compared with IPSO and IGSA with respect to optimize the path length from predefine initial position to designation position ,energy optimization in the terms of number of turn and arrival time.

      PubDate: 2016-01-11T05:12:06Z
      DOI: 10.1016/j.swevo.2015.10.011
      Issue No: Vol. 28 (2016)
       
  • Particle swarm and Box’s complex optimization methods to design linear
           tubular switched reluctance generators for wave energy conversion
    • Authors: R.P.G. Mendes; M.R.A. Calado; S.J.P.S. Mariano
      Pages: 29 - 41
      Abstract: Publication date: Available online 7 January 2016
      Source:Swarm and Evolutionary Computation
      Author(s): R.P.G. Mendes, M.R.A. Calado, S.J.P.S. Mariano
      This paper addresses the optimization of the linear switched reluctance generator with tubular topology to be applied in a sea wave energy conversion system. Two new algorithms to optimize the geometry of the generators are proposed. The algorithms are based on both particle swarm and Box´s complex optimization methods. The optimization procedures consist of a multidimensional optimal value search. First the initial variable vectors are specified throughout the feasible search space. Then, an iterative procedure is applied with the goal of finding the variable values that minimize the objective function. The proposed algorithms are suitable for the optimization problem considered since the objective function is highly nonlinear and not analytically defined, as evaluated using a finite element analysis based software, and show very good performance. A factor that characterizes the generation capabilities is also defined and the obtained optimized generators are compared.

      PubDate: 2016-01-11T05:12:06Z
      DOI: 10.1016/j.swevo.2015.12.003
      Issue No: Vol. 28 (2016)
       
  • Solving Multi-objective Portfolio Optimization Problem Using Invasive Weed
           Optimization
    • Authors: Amir Rezaei Pouya; Maghsud Solimanpur; Mustafa Jahangoshai Rezaee
      Pages: 42 - 57
      Abstract: Publication date: Available online 15 January 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Amir Rezaei Pouya, Maghsud Solimanpur, Mustafa Jahangoshai Rezaee
      Portfolio optimization is one of the important issues for effective and economic investment. There is plenty of research in the literature addressing this issue. Most of these pieces of research attempt to make the Markowitz’s primary portfolio selection model more realistic or seek to solve the model for obtaining fairly optimum portfolios. In this paper, P E criterion and Experts’ Recommendations on Market Sectors have been added to the primary Markowitz mean-variance model as two objectives. The P E ratio is one of the important criteria for investment in the stock market, which captures the current expectations of the market activists about different companies. Experts’ Recommendations for different Market Sectors, on the other hand, captures the experts’ predictions about the future of the stock market. There are many solving methods for the portfolio optimization problem, but almost none of them investigates Invasive Weed Optimization algorithm (IWO). In this research, the proposed multi-objective portfolio selection model has been transformed into a single-objective programming model using fuzzy normalization and uniform design method. Some guidelines are given for parameter setting in the proposed IWO algorithm. The model is then applied to monthly data of top 50 companies of Tehran Stock Exchange Market in 2013. The proposed model is then solved by three methods: (1) the proposed IWO algorithm, (2) the Particle Swarm Optimization algorithm (PSO), and (3) the Reduced Gradient Method (RGM). The non-dominated solutions of these algorithms are compared with each other using Data Envelopment Analysis (DEA). According to the comparisons, it can be concluded that IWO and PSO algorithms have the same performance in most important criteria, but IWO algorithm has better solving time than PSO algorithm and better performance in dominating inefficient solutions, and PSO algorithm has better results in total violation of constraints.

      PubDate: 2016-01-16T05:54:31Z
      DOI: 10.1016/j.swevo.2016.01.001
      Issue No: Vol. 28 (2016)
       
  • Ageist Spider Monkey Optimization Algorithm
    • Authors: Avinash Sharma; Akshay Sharma; B.K. Panigrahi; Deep Kiran; Rajesh Kumar
      Pages: 58 - 77
      Abstract: Publication date: Available online 18 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Avinash Sharma, Akshay Sharma, BK Panigrahi, Deep Kiran, Rajesh Kumar
      Swarm Intelligence (SI) is quite popular in the field of numerical optimization and has enormous scope for research. A number of algorithms based on decentralized and self-organized swarm behavior of natural as well as artificial systems, have been proposed and developed in last few years. Spider Monkey Optimization (SMO) algorithm, inspired by the intelligent behavior of spider monkeys, is one such recently proposed algorithm. The algorithm along with some of its variants has proved to be very successful and efficient. A spider monkey group consists of members from every age group. The agility and swiftness of the spider monkeys differ on the basis of their age groups. This paper proposes a new variant of SMO algorithm termed as Ageist Spider Monkey Optimization (ASMO) algorithm which seems more practical in biological terms and works on the basis of age difference present in spider monkey population. Experiments on different benchmark functions with different parameters and settings have been carried out and the variant with the best suited settings is proposed. This variant of SMO has enhanced the performance of its original version. Also, ASMO has performed better in comparison to some of the recent advanced algorithms.

      PubDate: 2016-02-23T08:30:35Z
      DOI: 10.1016/j.swevo.2016.01.002
      Issue No: Vol. 28 (2016)
       
  • A multilevel ACO approach for solving forest transportation planning
           problems with environmental constraints
    • Authors: Pengpeng Lin; Marco A. Contreras; Ruxin Dai; Jun Zhang
      Pages: 78 - 87
      Abstract: Publication date: Available online 3 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Pengpeng Lin, Marco A. Contreras, Ruxin Dai, Jun Zhang
      This paper presents a multilevel ant colony optimization (MLACO) approach to solve constrained forest transportation planning problems (CFTPPs). A graph coarsening technique is used to coarsen a network representing the problem into a set of increasingly coarser level problems. Then, a customized ant colony optimization (ACO) algorithm is designed to solve the CFTPP from coarser to finer level problems. The parameters of the ACO algorithm are automatically configured by evaluating a parameter combination domain through each level of the problem. The solution obtained by the ACO for the coarser level problems is projected into finer level problem components, which are used to help the ACO search for finer level solutions. The MLACO was tested on 20 CFTPPs and solutions were compared to those obtained from other approaches including a mixed integer programming (MIP) solver, a parameter iterative local search (ParamILS) method, and an exhaustive ACO parameter search method. Experimental results showed that the MLACO approach was able to match solution qualities and reduce computing time significantly compared to the tested approaches.

      PubDate: 2016-02-12T13:16:26Z
      DOI: 10.1016/j.swevo.2016.01.003
      Issue No: Vol. 28 (2016)
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: December 2016
      Source:Swarm and Evolutionary Computation, Volume 31


      PubDate: 2016-11-26T16:48:49Z
       
  • Rocchio algorithm-based particle initialization mechanism for effective
           PSO classification of high dimensional data
    • Authors: Anwar Ali Yahya; Addin Osman; Mohammad Said El-Bashir
      Abstract: Publication date: Available online 21 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Anwar Ali Yahya, Addin Osman, Mohammad Said El-Bashir
      In recent years, there has been a growing interest in applying Particle Swarm Optimization (PSO) to data classification. Nonetheless, due to the curse of dimensionality, the effectiveness of the PSO applied to high dimensional data classification becomes questionable. This paper proposes a novel specialized PSO initialization mechanism, developed specifically for PSO applications to high dimensional data classification. The proposed initialization mechanism is inspired by the center-based sampling theory, which argues that the center of the search space is a promising region for the initialization step in evolutionary algorithms. Furthermore, the proposed initialization mechanism is based on an information retrieval algorithm called Rocchio Algorithm (RA); that identifies the center region of the search space of data classification. To validate the proposed mechanism, RA-based PSO has been applied to a high dimensional classification task in educational data mining. More specifically, RA-based PSO has been applied to classify a dataset of teachers' classroom questions into Bloom's taxonomy cognitive levels. To do so, a dataset of teachers' classroom questions has been collected and annotated manually with Bloom's taxonomy cognitive levels. Pre-processing steps have been applied to convert questions into a representation suitable for classification. Using this dataset, the standard PSO, PSO with generic initialization mechanisms, and RA-based PSO have been experimented and compared. The results show a poor performance of the standard PSO and the PSO with the generic initialization mechanisms, as well as a significant improvement in the performance of RA-based PSO. These results indicate that a proper task-specific PSO initialization mechanism is crucial for effective PSO performance in high dimensional data classification. Furthermore, a comparison between RA-based PSO and pure RA classification provide a quantitative estimation of the role of initialization mechanism and PSO search for the classification of the dataset. On the other hand, the comparison between RA-based PSO approach and three conventional machine learning approaches, experimented on the same dataset confirms the effectiveness of RA-based PSO for high dimensional data classification. Moreover, the comparison between RA-based PSO approach and machine learning approaches, in terms of computational time efficiency, shows that they are comparable in classification time. However, as the learning of PSO is a time-consuming process, its effectiveness is significantly affected if the learning time is a matter.

      PubDate: 2016-11-26T16:48:49Z
      DOI: 10.1016/j.swevo.2016.11.005
       
  • Termite spatial correlation based particle swarm optimization for
           unconstrained optimization
    • Authors: Avinash Sharma; Rajesh Kumar; B.K. Panigrahi; Swagatam Das
      Abstract: Publication date: Available online 21 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Avinash Sharma, Rajesh Kumar, B.K. Panigrahi, Swagatam Das
      In last few years, swarm intelligence has become the mainstay in the field of continuous optimization with many researchers developing algorithms simulating swarm behavior for the purpose of numerical optimization. This work proposes a new Termite Spatial Correlation based Particle Swarm Optimization (TSC-PSO) algorithm inspired by the movement strategy shown within Termites (Cornitermes cumulans). TSC-PSO modifies the velocity equation in the original PSO algorithm by replicating the step correlation based termite motion mechanism that exhibits individually in nature and works with decentralized control to collectively perform the overall task. Further, the algorithm incorporates the mutation strategy within it to make it suitable to avoid stagnation conditions while performing optimization in complex search spaces. For deriving its utility various benchmark functions of different geometric properties have been used. Experiments clearly demonstrate the success of the proposed algorithm in different benchmark conditions against various state-of-the-art optimization algorithms.

      PubDate: 2016-11-26T16:48:49Z
      DOI: 10.1016/j.swevo.2016.11.001
       
  • Noisy Evolutionary Optimization Algorithms-A Comprehensive Survey
    • Authors: Pratyusha Rakshit; Amit Konar; Swagatam Das
      Abstract: Publication date: Available online 18 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Pratyusha Rakshit, Amit Konar, Swagatam Das
      Noisy optimization is currently receiving increasing popularity for its widespread applications in engineering optimization problems, where the objective functions are often found to be contaminated with noisy sensory measurements. In absence of knowledge of the noise-statistics, discriminating better trial solutions from the rest becomes difficult in the “selection” step of an evolutionary optimization algorithm with noisy objective/s. This paper provides a thorough survey of the present state-of-the-art research on noisy evolutionary algorithms for both single and multi-objective optimization problems. This is undertaken by incorporating one or more of the five strategies in traditional evolutionary algorithms. The strategies include i) fitness sampling of individual trial solution, ii) fitness estimation of noisy samples, iii) dynamic population sizing over the generations, iv) adaptation of the evolutionary search strategy, and v) modification in the selection strategy.

      PubDate: 2016-11-19T14:40:26Z
      DOI: 10.1016/j.swevo.2016.09.002
       
  • Portfolio optimization using novel co-variance guided Artificial Bee
           Colony algorithm
    • Authors: Divya Kumar; K.K. Mishra
      Abstract: Publication date: Available online 18 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Divya Kumar, K.K. Mishra
      Although the use of evolutionary algorithms and fuzzy logic for portfolio optimization is an established research area, this field remains fascinating because of its important financial aspects. The field is brisk and it trances as there always remain research issues which are yet to explore. The problem of portfolio optimization comprises of finding an optimal distribution of funds among various available securities so as to maximize the return and minimize the risk. Artificial Bee Colony (ABC) is one of the effectual and widely used optimization technique based on swarm intelligence. Mixing co-variance principles with ABC algorithm assists in quick convergence with more precision. This paper presents a novel co-variance guided Artificial Bee Colony algorithm for portfolio optimization. As portfolio optimization consists of simultaneous optimization of multiple conflicting objectives, this algorithm is named as Multi-objective Co-variance based ABC (M-CABC). The efficacy of the proposed algorithm is tested on benchmark problems of portfolio optimization from the OR-library. The results validate the adept performance of the proposed algorithm in finding various optimal trade-off solutions simultaneously handling realistic constraints. The article concludes with exhaustive post-result analysis and observatory remarks to bring out some of the crucial properties of optimal portfolios.

      PubDate: 2016-11-19T14:40:26Z
      DOI: 10.1016/j.swevo.2016.11.003
       
  • A production inventory model with price discounted fuzzy demand using an
           interval compared hybrid algorithm
    • Authors: Anindita Kundu; Partha Guchhait; Prasenjit Pramanik; Manas Kumar Maiti; Manoranjan Maiti
      Abstract: Publication date: Available online 19 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Anindita Kundu, Partha Guchhait, Prasenjit Pramanik, Manas Kumar Maiti, Manoranjan Maiti
      An economic production quantity (EPQ) model in an imprecise environment is proposed, where the production rate, planning horizon and demand coefficients are fuzzy in nature. At the beginning of each cycle a price discount is offered for a period to boost the demand. During this period, demand increases with time depending upon the amount of discount. Here, demand also depends on the unit selling price. After withdrawal of the price discount, demand depends only on the unit selling price. The governing differential equation for the model is obviously fuzzy in nature as the production rate and demand are fuzzy. For this reason, the model is formulated using a fuzzy differential equation and the α-cut of the total profit from the planning horizon is obtained using fuzzy Riemann integration. To optimize the interval objective function, using a fuzzy preference relation on intervals and a fuzzy possibility/necessity measure, a hybrid algorithm with varying population size is developed by combining the features of particle swarm optimization (PSO) and a genetic algorithm (GA). This algorithm is named Interval Compared Hybrid Particle Swarm-Genetic Algorithm (ICHPSGA) and is used to find an optimal decision for the decision maker (DM) in different cases of the model. To test the efficiency of the algorithm, it is compared with two other established algorithms namely PSO and PSGA. Numerical experiments are performed to illustrate the model and some interesting observations are made.

      PubDate: 2016-11-19T14:40:26Z
      DOI: 10.1016/j.swevo.2016.11.004
       
  • A novel evolutionary rigid body docking algorithm for medical image
           registration
    • Authors: Rutuparna Panda; Sanjay Agrawal; Madhusmita Sahoo; Rajashree Nayak
      Abstract: Publication date: Available online 9 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Rutuparna Panda, Sanjay Agrawal, Madhusmita Sahoo, Rajashree Nayak
      One of today's motivating medical image processing problem is registration. Medical images acquired from different modalities give rise to a practical problem in image registration. For many years, mutual information has been utilized as a similarity criterion for registration. Therefore, it is believed to be the state-of-the-art in this field. Nonetheless, it is understood to be non-convex and possess many local maxima. To overcome this problem, we introduce a novel evolutionary rigid body docking (ERBD) algorithm for medical image registration. Here, the ligand is taken as the non-aligned (target) image (to be registered) and protein as the reference image. The alignment of the target image is changed as per the optimal configuration. This paper introduces a firsthand objective (fitness) function for finding optimal configurations. Genetic algorithm (GA) is used to optimize the fitness function. The proposed method is useful for recovery of rotation and translation parameters. Different image data formats like magnetic resonance image (MRI), computed tomography (CT) and positron emission tomography (PET) images from Retrospective Image Registration Evaluation (RIRE) project are used to demonstrate the effectiveness of our proposed method. Experimental results are presented to reveal the fact that our approach seems to be efficient.

      PubDate: 2016-11-13T14:13:29Z
      DOI: 10.1016/j.swevo.2016.11.002
       
  • An Elitist Nondominated Sorting Genetic Algorithm for QoS Multicast
           Routing in Wireless Networks
    • Authors: Zaheeruddin; D.K. Lobiyal; Sunita Prasad
      Abstract: Publication date: Available online 4 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Zaheeruddin, D.K. Lobiyal, Sunita Prasad
      Due to the increasing popularity of real time multimedia applications, Quality-of-Service (QoS) based multicast routing has emerged as an active area of research. The fundamental requirements of many multimedia applications are cost minimization and bounded end-to-end delay. In addition, video data traffic is sensitive to packet loss and delay variance. Hence, multiobjective optimization seems to be the most appropriate method for such complex problems. We, therefore, formulate QoS based multicast routing as a multiobjective optimization problem using Elitist Nondominated Sorting Genetic Algorithm (NSGA-II). To enhance the performance of NSGA-II, we propose a new encoding scheme that aims to achieve a diversified solution set and faster convergence of search towards optimal Pareto front. It has also been observed that identical solutions cause loss of diversity which degrades the performance of NSGA-II algorithm. To overcome this drawback, the second enhancement based on replacement strategy is used. In this approach, one copy of identical solution is retained and new random solutions are introduced in the population to obtain a well distributed Pareto front. The results of new encoding scheme and replacement strategy are compared with other existing evolutionary multiobjective algorithms to demonstrate the effectiveness of the proposed approach. To further strengthen the usefulness of modified algorithm, the experimental results are validated using statistical significance tests.

      PubDate: 2016-11-05T13:32:45Z
      DOI: 10.1016/j.swevo.2016.10.004
       
  • On the application of search-based techniques for software engineering
           predictive modeling: A systematic review and future directions
    • Authors: Ruchika Malhotra; Megha Khanna; Rajeev R. Raje
      Abstract: Publication date: Available online 20 October 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Ruchika Malhotra, Megha Khanna, Rajeev R. Raje
      Software engineering predictive modeling involves construction of models, with the help of software metrics, for estimating quality attributes. Recently, the use of search-based techniques have gained importance as they help the developers and project-managers in the identification of optimal solutions for developing effective prediction models. In this paper, we perform a systematic review of 78 primary studies from January 1992 to December 2015 which analyze the predictive capability of search-based techniques for ascertaining four predominant software quality attributes, i.e., effort, defect proneness, maintainability and change proneness. The review analyses the effective use and application of search-based techniques by evaluating appropriate specifications of fitness functions, parameter settings, validation methods, accounting for their stochastic natures and the evaluation of developmental models with the use of well-known statistical tests. Furthermore, we compare the effectiveness of different models, developed using the various search-based techniques amongst themselves, and also with the prevalent machine learning techniques used in literature. Although there are very few studies which use search-based techniques for predicting maintainability and change proneness, we found that the results of the application of search-based techniques for effort estimation and defect prediction are encouraging. Hence, this comprehensive study and the associated results will provide guidelines to practitioners and researchers and will enable them to make proper choices for applying the search-based techniques to their specific situations.

      PubDate: 2016-10-29T13:13:26Z
      DOI: 10.1016/j.swevo.2016.10.002
       
  • An efficient gbest-guided Cuckoo Search algorithm for higher order two
           channel filter bank design
    • Authors: Supriya Dhabal; Palaniandavar Venkateswaran
      Abstract: Publication date: Available online 20 October 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Supriya Dhabal, Palaniandavar Venkateswaran
      This paper proposes a new algorithm based on Gbest-guided Cuckoo Search (GCS) algorithm for the design of higher order Quadrature Mirror Filter (QMF) bank. Although the optimization of lower order filters can be performed easily by applying existing meta-heuristic optimization techniques like Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) etc., these methods are unsuccessful in searching higher order filter coefficients due to multimodality and nonlinear problem space; leads to some undesirable behaviors in filter responses like ripples in transition band, lower stop-band attenuation etc.. Comparison with other available results in the literature indicate that the proposed method exhibits an 69.02% increase in stop-band attenuation and 99.71% reduction in Perfect Reconstruction Error (PRE) of higher order filter bank. Besides, the percentage improvements in Fitness Function Evaluations (FFEs) of GCS based 55th order QMF bank design with respect to PSO, ABC and CSA are 81%, 82% and 59% respectively, and execution time is improved by 73%, 72% and 42% respectively. The simulation results also reveal that the proposed approach exhibits lowest mean and variance in different assessment parameters of filter bank and it does not require tuning of algorithmic parameters whereas in standard CSA replacement factor need to be adjusted. Further, the proposed algorithm is tested on six standard benchmark problems and complex benchmark functions from the CEC 2013 where it demonstrated significant performance improvements than other existing methods.

      PubDate: 2016-10-29T13:13:26Z
      DOI: 10.1016/j.swevo.2016.10.003
       
  • Quasi-oppositional symbiotic organism search algorithm applied to load
           frequency control
    • Authors: Dipayan Guha; Provas Roy; Subrata Banerjee
      Abstract: Publication date: Available online 13 October 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Dipayan Guha, Provas Roy, Subrata Banerjee
      The present work approaches a relatively new optimization scheme called “quasi-oppositional symbiotic organism search (QOSOS) algorithm”, for the first time, to find an optimal and effective solution for load frequency control (LFC) problem of the power system. The symbiotic organism search (SOS) algorithm works on the effect of symbiotic interaction strategies adopted by an organism to survive and propagate in the ecosystem. To avoid the suboptimal solution and to accelerate the convergence speed, the theory of quasi-oppositional based learning (Q-OBL) is integrated with original SOS and used to solve the LFC problem. To demonstrate the effectiveness of QOSOS algorithm, two-area interconnected power system with nonlinearity effect of governor dead band and generation rate constraint is considered at the first instant, followed by the four-area power system showing the consequence of load perturbation. The structural simplicity, robust performance and acceptability of well-popular proportional-integral-derivative (PID) controller enforce to implement it as a secondary controller for the present analysis. The success of QOSOS algorithm is established by comparing the dynamic performances of concerned power system with those obtained by some recently published algorithms available in the literature. Furthermore, the robustness and sensitivity are analyzed for the concerned power system to judge the efficacy of the proposed QOSOS approach.

      PubDate: 2016-10-15T09:55:28Z
      DOI: 10.1016/j.swevo.2016.10.001
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: October 2016
      Source:Swarm and Evolutionary Computation, Volume 30


      PubDate: 2016-09-25T09:40:37Z
       
  • A New Multi-objective Evolutionary Framework for Community Mining in
           Dynamic Social Networks
    • Authors: Bara'a A. Attea; Haidar S. Khoder
      Abstract: Publication date: Available online 20 September 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Bara'a A. Attea, Haidar S. Khoder
      Evolutionary clustering – clustering in the presence of dynamic shifts of data's topological structure – has recently drawn remarkable attention wherein several algorithms are developed in the study of complex real networks. Despite the growing interests, all of the algorithms are designed based on seemingly the same principle. The primary principle in these evolutionary clustering frameworks is guided by decomposing the problem into two individual criteria, snapshot quality and temporal smoothness. Snapshot quality should properly cluster individuals of a network into interconnected communities. Temporal smoothness, on the other hand, should capture well the dynamic shift of the interconnected clusters from one time step to another. Thus, in the absence of any dynamic behavior, an evolutionary clustering model should be no more than a community detection one in a static network. Unfortunately, all of the developed algorithms are proposed based on discretion of the snapshot quality as a unified of both intra- and inter- connected community detection model while temporal cost as a community evolution detection model. The contribution of this paper starts by noting the limitation of the existing state-of-the-art algorithms. Despite their performance on dynamic complex networks, their formulations lack complete reflection of sufficient community detection model. Our framework, then, models the evolutionary clustering problem by hypothesizing that it should not depart too much from the community detection problem. To support this claim, an alternate decomposition perspective is proposed by projecting the problem, as a multi-objective optimization problem, in the light of snapshot and temporal of both intra- and inter-community scores. Two snapshot qualities are proposed to individually emphasize the role of intra- and inter- community scores, while temporal cost is proposed to cross-fertilize inter- community score. By applying one of the prominent multi-objective evolutionary algorithms (MOEAs) to solve the proposed multi-objective evolutionary clustering framework and testing it on several synthetic and real-world dynamic networks, we demonstrate the ability of the proposed model to address the problem more accurately than the existing state-of-the-art formulations.

      PubDate: 2016-09-20T09:18:08Z
      DOI: 10.1016/j.swevo.2016.09.001
       
  • Screening dense and noisy DOX-datasets with NN-blending and “dizzy”
           swarm intelligence: Profiling a water quality process
    • Authors: George J. Besseris
      Abstract: Publication date: Available online 24 August 2016
      Source:Swarm and Evolutionary Computation
      Author(s): George J. Besseris
      A novel nature-inspired method is presented in this work for resolving product/process development or improvement with design of experiments (DOX). The technique is suitable for difficult Taguchi-type multifactorial screening and optimization studies that need to simultaneously contain the double hassle of controllable and uncontrollable noise intrusions. The three-part sequential processing routine requires: 1) a regressive data-compression preprocessing, 2) a smart-sample generation using general-regression neural networks (GRNN), and 3) a screening power prediction using ‘reverse’ swarm intelligence (SI). The approach is primed to confront potential non-linearity and data messiness in the examined effects. The Taguchi-type orthogonal-array (OA) sampler is tuned for retrieving information in controllable (outer OA) and uncontrollable (inner OA) noises. The OA-saturation condition is elicited for maximum data exploitation. GRNN-fuzzification consolidates into a single contribution the uncertainty from all possible sources. The resulting ‘smart’ sample is defuzzified by a robust-and-agile data reduction. Screening-solution meta-power is controlled with a new SI-variant. The independent swarm groups, as many as the studied effects, are tracked toward preassigned targets, i.e. their ability to return to their host beehives. The technique is illustrated on a complex purification process where published multifactorial data had been collected for a critical wastewater paradigm and thus may be used to test the benchmark solution. However, environmental water-qualimetrics are profoundly dominated by messy data as justified in this work. We elucidate on several issues that regular Taguchi methods may be benefited by the proposed GRNN/SI processing while emphasizing the consequence of overlooking the underlying assumptions that govern standard comparison models. The new swarm itelligence method offered a practical way to estimate a first-time “soft” power measure for the inner/outer OA optimization case that was impossible with ordinary statistical multi-factorial treatments. Key performance advantages in efficiency, robustness and convenience are highlighted against alternative approaches.

      PubDate: 2016-08-27T01:40:21Z
      DOI: 10.1016/j.swevo.2016.08.003
       
  • Evolutionary and GPU computing for topology optimization of structures
    • Authors: Laxman Ram; Deepak Sharma
      Abstract: Publication date: Available online 24 August 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Laxman Ram, Deepak Sharma
      Although Structural topology Optimization, as a discrete optimization problem, has been successfully solved several times in the literature using evolutionary algorithms (EAs), the two key difficulties lie in generating geometrically feasible structures and handling a high computation time. These two challenges are addressed in this paper by adopting triangular representation for two-dimensional continuum structures, related crossover and mutation operators, and by performing computations in parallel on the graphics processing unit (GPU). Two case studies are solved on the GPU that show 5× of speedup over CPU implementation. The parametric study on the population size of EA shows that the approximate Pareto-optimal solutions can be evolved using a small population with the proposed EA operators.

      PubDate: 2016-08-27T01:40:21Z
      DOI: 10.1016/j.swevo.2016.08.004
       
  • BFOA-scaled fractional order fuzzy PID controller applied to AGC of
           multi-area multi-source electric power generating systems
    • Authors: Yogendra Arya; Narendra Kumar
      Abstract: Publication date: Available online 12 August 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Yogendra Arya, Narendra Kumar
      In the fast developing world of today, automatic generation control (AGC) plays an incredibly significant role in offering inevident demand of good quality power supply in power system. To deliver a quality power, AGC system requires an efficient and intelligent control algorithm. Hence, in this paper, a novel fractional order fuzzy proportional-integral-derivative (FOFPID) controller is proposed for AGC of electric power system. The proposed controller is tested for the first time on three structures of multi-area multi-source AGC system. The gains and fractional order parameters such as order of integrator (λ) and differentiator (µ and γ) of FOFPID controller are optimized using bacterial foraging optimization algorithm (BFOA). Initially, the proposed controller is implemented on a traditional two-area multi-source hydrothermal power system and its effectiveness is established by comparing the results with FOPID, fuzzy PID (FPID) and PI/PID controller based on recently published optimization techniques like hybrid firefly algorithm-pattern search (hFA-PS) and grey wolf optimization (GWO) algorithm. The approach is further extended to restructured multi-source hydrothermal and thermal gas systems. It is observed that the dynamic performance of the proposed BFOA optimized FOFPID controller is superior to BFOA optimized FPID/FOPID/PID and differential evolution/genetic algorithm optimized PID controllers. It is also detected that the dynamic responses obtained under different power transactions with/without appropriate generation rate constraint, time delay and governor dead-zone effectively satisfy the AGC requirement in deregulated environment. Moreover, robustness of the proposed approach is verified against wide variations in the nominal initial loading, system parameters, distribution company participation matrix structure and size and position of uncontracted power demand.

      PubDate: 2016-08-16T23:55:41Z
      DOI: 10.1016/j.swevo.2016.08.002
       
  • Integrated frequency and power control of an isolated hybrid power system
           considering scaling factor based fuzzy classical controller
    • Authors: Somnath. Ganguly; Tarkeshwar Mahto; V Mukherjee
      Abstract: Publication date: Available online 9 August 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Somnath. Ganguly, Tarkeshwar Mahto, V Mukherjee
      This paper describes an application of quasi-oppositional harmony search (QOHS) algorithm to design the scaling factor (SF) based fuzzy classical controller (such as PI/PD/PID) for frequency and power control of an isolated hybrid power system (IHPS). The considered IHPS model is comprised of a wind turbine generator, a diesel engine generator and an energy storage device (such as superconducting magnetic energy storage (SMES), in this case). Traditionally, SF, membership functions and control rules are obtained in fuzzy logic controllers (FLCs) by trial and error method or are obtained based on the experiences of the designers or are optimized by some traditional optimization techniques with some extra computational cost. To overcome all these problems of FLCs, classical controllers have been integrated in this paper with the FLC. QOHS algorithm is applied to simultaneously tune the SFs (the only tunable parameter of FLC), the gains of the classical controllers and the tunable parameters of the SMES device to minimize frequency and power deviations of the studied IHPS system against various load and wind change. Different considered controller configurations of the IHPS are SF based FLC (termed as Fuzzy-only), SF based FLC with proportional-integral (PI) (named as Fuzzy-PI) controller, SF based FLC with proportional-derivative (PD) (abbreviated as Fuzzy-PD) controller and SF based FLC with proportional-integral-derivative (PID) (designated as Fuzzy-PID) controller. Simulation results, explicitly, show that the performance of the Fuzzy-PID controller based IHPS is superior to Fuzzy-only, Fuzzy-PI and Fuzzy-PD controller based IHPS configuration in terms of overshoot, settling time and the proposed Fuzzy-PID controller is robust against various wide range of load changes.

      PubDate: 2016-08-11T23:37:02Z
      DOI: 10.1016/j.swevo.2016.08.001
       
  • Gravitational Swarm Optimizer for Global Optimization
    • Authors: Anupam Yadav; Kusum Deep; Joong Hoon Kim; Atulya K. Nagar
      Abstract: Publication date: Available online 2 August 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Anupam Yadav, Kusum Deep, Joong Hoon Kim, Atulya K Nagar
      In this article, a new meta-heuristic method is proposed by combining particle swarm optimization (PSO) and gravitational search in a coherent way. The advantage of swarm intelligence and the idea of a force of attraction between two particles are employed collectively to propose an improved meta-heuristic method for constrained optimization problems. Excellent constraint handling is always required for the success of any constrained optimizer. In view of this, an improved constraint-handling method is proposed which was designed in alignment with the constitutional mechanism of the proposed algorithm. The design of the algorithm is analyzed in many ways and the theoretical convergence of the algorithm is also established in the article. The efficiency of the proposed technique was assessed by solving a set of 24 constrained problems and 15 unconstrained problems which have been proposed in IEEE-CEC sessions 2006 and 2015, respectively. The results are compared with 11 state-of-the-art algorithms for constrained problems and 6 state-of-the-art algorithms for unconstrained problems. A variety of ways are considered to examine the ability of the proposed algorithm in terms of its converging ability, success, and statistical behavior. The performance of the proposed constraint-handling method is judged by analyzing its ability to produce a feasible population. It was concluded that the proposed algorithm performs efficiently with good results as a constrained optimizer.
      Graphical abstract image Highlights

      PubDate: 2016-08-06T22:52:04Z
      DOI: 10.1016/j.swevo.2016.07.003
       
  • Effective heuristics for ant colony optimization to handle large-scale
           problems
    • Authors: Hassan Ismkhan
      Abstract: Publication date: Available online 22 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Hassan Ismkhan
      Although ant colony optimization (ACO) has successfully been applied to a wide range of optimization problems, its high time- and space-complexity prevent it to be applied to the large-scale instances. Furthermore, local search, used in ACO to increase its performance, is applied without using heuristic information stored in pheromone values. To overcome these problems, this paper proposes new strategies including effective representation and heuristics, which speed up ACO and enable it to be applied to large-scale instances. Results show that in performed experiments, proposed ACO has better performance than other versions in terms of accuracy and speed.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.06.006
       
  • Genetic algorithms to balanced tree structures in graphs
    • Authors: Riham Moharam; Ehab Morsy
      Abstract: Publication date: Available online 22 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Riham Moharam, Ehab Morsy
      Given an edge-weighted graph G = ( V , E ) with vertex set V and edge set E, we study in this paper the following related balanced tree structure problems in G. The first problem, called Constrained Minimum Spanning Tree Problem (CMST), asks for a rooted tree T in G that minimizes the total weight of T such that the distance between the root and any vertex v in T is at most a given constant C times the shortest distance between the two vertices in G. The Constrained Shortest Path Tree Problem (CSPT) requires a rooted tree T in G that minimizes the maximum distance between the root and all vertices in V such that the total weight of T is at most a given constant C times the minimum tree weight in G. The third problem, called Minimum Maximum Stretch Spanning Tree (MMST), looks for a tree T in G that minimize the maximum distance between all pairs of vertices in V. It is easy to conclude from the literatures that the above problems are NP-hard. We present efficient genetic algorithms that return (as shown by our experimental results) high quality solutions for these problems.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.06.005
       
  • A comprehensive survey of traditional, merge-split and evolutionary
           approaches proposed for determination of cluster number
    • Authors: Emrah Hancer; Dervis Karaboga
      Abstract: Publication date: Available online 23 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Emrah Hancer, Dervis Karaboga
      Today's data mostly does not include the knowledge of cluster number. Therefore, it is not possible to use conventional clustering approaches to partition today's data, i.e., it is necessary to use the approaches that automatically determine the cluster number or cluster structure. Although there has been a considerable attempt to analyze and categorize clustering algorithms, it is difficult to find a survey paper in the literature that has thoroughly focused on the determination of cluster number. This significant issue motivates us to introduce concepts and review methods related to automatic cluster evolution from a theoretical perspective in this study.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.06.004
       
  • Swarm and evolutionary computing algorithms for system identification and
           filter design: A comprehensive review
    • Authors: Akhilesh Gotmare; Sankha Subhra Bhattacharjee; Rohan Patidar; Nithin V. George
      Abstract: Publication date: Available online 23 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Akhilesh Gotmare, Sankha Subhra Bhattacharjee, Rohan Patidar, Nithin V. George
      An exhaustive review on the use of structured stochastic search approaches towards system identification and digital filter design is presented in this paper. In particular, the paper focuses on the identification of various systems using infinite impulse response adaptive filters and Hammerstein models as well as on the estimation of chaotic systems. In addition to presenting a comprehensive review on the various swarm and evolutionary computing schemes employed for system identification as well as digital filter design, the paper is also envisioned to act as a quick reference for a few popular evolutionary computing algorithms.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.06.007
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: August 2016
      Source:Swarm and Evolutionary Computation, Volume 29


      PubDate: 2016-08-01T22:20:16Z
       
  • Experimentation investigation of abrasive water jet machining parameters
           using Taguchi and Evolutionary optimization techniques
    • Authors: Rajkamal Shukla; Dinesh Singh
      Abstract: Publication date: Available online 26 July 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Rajkamal Shukla, Dinesh Singh
      In the last decade, numerous new materials are rapidly emerging and developed; it creates considerable interest in the researcher to search out the optimum combination of machining parameters during machining of these materials using advanced machining processes (AMP). In this work, an experimental investigation is carried out on abrasive water jet machining (AWJM) process for the machining of material AA631-T6 using the Taguchi methodology. Parameters such as transverse speed, standoff distance and mass flow rate are considered to obtain the influence of these parameters on kerf top width and taper angle. Regression models have been developed to correlate the data generated using experimental results. Seven advanced optimization techniques, i.e., particle swarm optimization, firefly algorithm, artificial bee colony, simulated annealing, black hole, biogeography based and non-dominated sorting genetic algorithm are attempted for the considered AWJM process. The effectiveness of these algorithms is compared and found that bio-geography algorithm is performing better compared to other algorithms. Furthermore, a non-dominated set of solution is obtained to have diversity in the solutions for the AWJM process. The result obtained using the Taguchi method and optimization techniques are confirmed using confirmation experiments. Confirmatory experiments show that both the optimization techniques and Taguchi method are the effective tools in optimizing the process parameters of the AWJM process.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.07.002
       
  • Swarm intelligence inspired classifiers for facial recognition
    • Authors: Salima Nebti; Abdallah Boukerram
      Abstract: Publication date: Available online 9 July 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Salima Nebti, Abdallah Boukerram
      Facial recognition is a challenging issue in pattern recognition arising from the need for high security systems capable of overcoming the variability of the acquisition environment such as illumination, pose or facial expression. A broad range of recognition methods have been suggested, yet most are still unable to yield optimal accuracy. More recently, new methods based on swarm intelligence or classifiers combination have been devised in the field of facial recognition. Swarm intelligence based methods aim to achieve effective recognition accuracy by exploiting their global optimization capability. The combination of classifiers is a new trend allowing cooperation between multiple classifiers. In this work, two classifiers inspired from swarm intelligence are proposed: a bees algorithm based classifier and a decision tree based binary particle swarm optimization classifier. The two are then combined with a decision tree based fuzzy support vector machine by using the majority vote as an attempt to compensate for the weakness of single classifiers. Moreover, the impact of different characteristic features and space reduction methods has been examined namely, the Gabor magnitude and the Gabor phase congruency features in combination with PCA, LDA or KFA reduction space methods. The experiments were conducted on four popular databases: ORL, YALE, FERET and UMIST. The results revealed that the proposed swarm intelligence based classifiers are very effective compared to similar classifiers in terms of recognition accuracy.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.07.001
       
  • Computing with the Collective Intelligence of Honey Bees – A Survey
    • Authors: Anguluri Rajasekhar; Nandar Lynn; Swagatam Das; P.N. Suganthan
      Abstract: Publication date: Available online 15 July 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Anguluri Rajasekhar, Nandar Lynn, Swagatam Das, P.N. Suganthan
      Over past few decades, families of algorithms based on the intelligent group behaviors of social creatures like ants, birds, fishes, and bacteria have been extensively studied and applied for computer-aided optimization. Recently there has been a surge of interest in developing algorithms for search, optimization, and communication by simulating different aspects of the social life of a very well-known creature: the honey bee. Several articles reporting the success of the heuristics based on swarming, mating, and foraging behaviors of the honey bees are being published on a regular basis. In this paper we provide a brief but comprehensive survey of the entire horizon of research so far undertaken on the algorithms inspired by the honey bees. Starting with the biological perspectives and motivations, we outline the major bees-inspired algorithms, their prospects in the respective problem domains and their similarities and dissimilarities with the other swarm intelligence algorithms. We also provide an account of the engineering applications of these algorithms. Finally we identify some open research issues and promising application areas for the bees-inspired computing techniques.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.06.001
       
  • A quantum-inspired genetic algorithm for solving the antenna positioning
           problem
    • Authors: Zakaria Abd El Moiz Dahi; Chaker Mezioud; Amer Draa
      Abstract: Publication date: Available online 16 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Zakaria Abd El Moiz Dahi, Chaker Mezioud, Amer Draa
      Cellular phone networks are one of today's most popular means of communication. The big popularity and accessibility of the services proposed by these networks have made the mobile industry a field with high standard and competition where service quality is key. Actually, such a quality is strongly bound to the design quality of the networks themselves, where optimisation issues exist at each step. Thus, any process that cannot cope with these problems may alter the design phase and ultimately the service provided. The Antenna Positioning Problem (APP) is one of the most determinant optimisation issues that engineers face during network life cycle. This paper proposes a new variant of the Quantum-Inspired Genetic Algorithm (QIGA) based on a novel quantum gate for solving the APP. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. Several statistical analysis tests have been carried as well. State-of-the-art algorithms designed to solve the APP, the Population-Based Incremental Learning (PBIL) and Genetic Algorithm (GA), are taken as a comparison basis. Performance evaluation of the proposed approach proves that it is efficient, robust and scalable; it could outperform both PBIL and GA in many benchmark instances.

      PubDate: 2016-06-18T18:03:36Z
      DOI: 10.1016/j.swevo.2016.06.003
       
  • The influence of population size in geometric semantic GP
    • Authors: Mauro Castelli; Luca Manzoni; Sara Silva; Leonardo Vanneschi; Aleš Popovič
      Abstract: Publication date: Available online 3 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Mauro Castelli, Luca Manzoni, Sara Silva, Leonardo Vanneschi, Aleš Popovič
      In this work, we study the influence of the population size on the learning ability of Geometric Semantic Genetic Programming for the task of symbolic regression. A large set of experiments, considering different population size values on different regression problems, has been performed. Results show that, on real-life problems, having small populations results in a better training fitness with respect to the use of large populations after the same number of fitness evaluations. However, performance on the test instances varies among the different problems: in datasets with a high number of features, models obtained with large populations present a better performance on unseen data, while in datasets characterized by a relative small number of variables a better generalization ability is achieved by using small population size values. When synthetic problems are taken into account, large population size values represent the best option for achieving good quality solutions on both training and test instances.

      PubDate: 2016-06-13T02:27:18Z
      DOI: 10.1016/j.swevo.2016.05.004
       
  • PEAR: a massively parallel evolutionary computational approach for
           political redistricting optimization and analysis
    • Authors: Yan Liu; Wendy Tam Cho Shaowen Wang
      Abstract: Publication date: Available online 26 May 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Yan Y. Liu, Wendy K. Tam Cho, Shaowen Wang
      Political redistricting, a well-known problem in political science and geographic information science, can be formulated as a combinatorial optimization problem, with objectives and constraints defined to meet legal requirements. The formulated optimization problem is NP-hard. We develop a scalable evolutionary computational approach utilizing massively parallel high performance computing for political redistricting optimization and analysis at fine levels of granularity. Our computational approach is based in strong substantive knowledge and deep adherence to Supreme Court mandates. Since the spatial configuration plays a critical role in the effectiveness and numerical efficiency of redistricting algorithms, we have designed spatial evolutionary algorithm (EA) operators that incorporate spatial characteristics and effectively search the solution space. Our parallelization of the algorithm further harnesses massive parallel computing power provided by supercomputers via the coupling of EA search processes and a highly scalable message passing model that maximizes the overlapping of computing and communication at runtime. Experimental results demonstrate desirable effectiveness and scalability of our approach (up to 131K processors) for solving large redistricting problems, which enables substantive research into the relationship between democratic ideals and phenomena such as partisan gerrymandering.

      PubDate: 2016-06-13T02:27:18Z
       
  • Review of differential evolution population size
    • Authors: Adam P. Piotrowski
      Abstract: Publication date: Available online 4 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Adam P. Piotrowski
      Population size of Differential Evolution (DE) algorithms is often specified by user and remains fixed during run. During the first decade since the introduction of DE the opinion that its population size should be related to the problem dimensionality prevailed, later the approaches to DE population size setting diversified. In large number of recently introduced DE algorithms the population size is considered to be problem-independent and often fixed to 100 or 50 individuals, but alongside a number of DE variants with flexible population size have been proposed. The present paper briefly reviews the opinions regarding DE population size setting and verifies the impact of the population size on the performance of DE algorithms. Ten DE algorithms with fixed population size, each with at least five different population size settings, and four DE algorithms with flexible population size are tested on CEC2005 benchmarks and CEC2011 real-world problems. It is found that the inappropriate choice of the population size may severely hamper the performance of each DE algorithm. Although the best choice of the population size depends on the specific algorithm and problem to be solved, some rough guidelines may be sketched. For low-dimensional problems (with dimensionality below 30) the population size equal to 100 individuals is suggested; population sizes smaller than 50 are rarely advised. For higher-dimensional artificial problems the population size should often depend on the problem dimensionality d and be set to 3d–5d. Unfortunately, setting proper population size for higher-dimensional real-world problems (d > 40) turns out too problem and algorithm-dependent to give any general guide; 200 individuals may be a first guess, but many DE approaches would need a much different choice, ranging from 50 to 10d. However, quite clear relation between the population size and the convergence speed has been found, showing that the fewer function calls are available, the lower population sizes perform better. Based on the extensive experimental results the use of adaptive population size is highly recommended, especially for higher-dimensional and real-world problems. However, which specific algorithms with population size adaptation perform better depends on the number of function calls allowed.

      PubDate: 2016-06-13T02:27:18Z
      DOI: 10.1016/j.swevo.2016.05.003
       
  • A competitive memetic algorithm for multi-objective distributed
           permutation flow shop scheduling problem
    • Authors: Jin Deng; Ling Wang
      Abstract: Publication date: Available online 8 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Jin Deng, Ling Wang
      In this paper, a competitive memetic algorithm (CMA) is proposed to solve the multi-objective distributed permutation flow-shop scheduling problem (MODPFSP) with the makespan and total tardiness criteria. Two populations corresponding to two different objectives are employed in the CMA. Some objective-specific operators are designed for each population, and a special interaction mechanism between two populations is designed. Moreover, a competition mechanism is proposed to adaptively adjust the selection rates of the operators, and some knowledge-based local search operators are developed to enhance the exploitation ability of the CMA. In addition, the influence of the parameters on the performance of the CMA is investigated by using the Taguchi method of design-of-experiment. Finally, extensive computational tests and comparisons are carried out to demonstrate the effectiveness of the CMA in solving the MODPFSP.

      PubDate: 2016-06-13T02:27:18Z
      DOI: 10.1016/j.swevo.2016.06.002
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: June 2016
      Source:Swarm and Evolutionary Computation, Volume 28


      PubDate: 2016-05-07T23:42:42Z
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.205.140.252
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2016