for Journals by Title or ISSN
for Articles by Keywords
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: 5.631]   [H-I: 13]   [1 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2210-6502
   Published by Elsevier Homepage  [2970 journals]
  • Node-depth Phylogenetic-Based Encoding, a Spanning-Tree Representation for
           Evolutionary Algorithms. Part I: Proposal and Properties Analysis
    • Abstract: Publication date: Available online 13 May 2016
      Source:Swarm and Evolutionary Computation
      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: 2016-05-14T00:11:44Z
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: June 2016
      Source:Swarm and Evolutionary Computation, Volume 28

      PubDate: 2016-05-07T23:42:42Z
  • Using autonomous search for solving constraint satisfaction problems via
           new modern approaches
    • 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
  • An object tracking method using modified galaxy-based search algorithm
    • 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
  • COOA: Competitive Optimization Algorithm
    • 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
  • Bio-inspired search algorithms for unstructured P2P overlay networks
    • 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
  • Hybrid self-adaptive Cuckoo search for global optimization
    • 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
  • Fuzzy Evolutionary Cellular Learning Automata model for text summarization
    • Abstract: Publication date: Available online 1 April 2016
      Source:Swarm and Evolutionary Computation
      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: 2016-04-01T15:30:25Z
  • Hybrid HSA and PSO algorithm for energy efficient cluster head selection
           in wireless sensor networks
    • Abstract: Publication date: Available online 25 March 2016
      Source:Swarm and Evolutionary Computation
      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: 2016-03-27T15:00:24Z
  • Quantum Inspired Social Evolution (QSE) Algorithm for 0-1 Knapsack problem
    • 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
  • Multi-dimensional signaling method for population-based metaheuristics:
           Solving the large-scale scheduling problem in smart grids
    • 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
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: April 2016
      Source:Swarm and Evolutionary Computation, Volume 27

      PubDate: 2016-03-12T16:17:45Z
  • A New Cuckoo Search Algorithm for 2-Machine Robotic Cell Scheduling
           Problem with Sequence-Dependent Setup Times
    • Abstract: Publication date: Available online 11 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Arindam Majumder, Dipak Laha
      The paper addresses the problem of 2-machine robotic cell scheduling of one-unit cycle with sequence-dependent setup times and different loading/unloading times of the parts. As an alternative metaheuristic algorithm, the cuckoo search algorithm has recently attracted growing interests of researchers. It has the capability to search globally as well as locally to converge to the global optimality by exploring the search space more efficiently for to its global random walk governed by Levy flights, rather than standard isotropic random walk. In this study, a discrete cuckoo search algorithm is proposed to determine the sequence of robot moves along with the sequence of parts so that the cycle time is minimized. In the proposed algorithm, the fractional scaling factor based procedure is presented to determine the step length of Levy flights distribution in discrete from and then, using this step length, two neighborhood search techniques, interchange and cyclical shift methods are applied to the current solution to obtain improved solution. A response surface methodology based on desirability function is used to enhance the convergence speed of the proposed algorithm. Also, a design of experiment is employed to tune the operating parameters of the algorithm. Finally, empirical results with a large number of randomly generated problem instances involving large part sizes varying from 200 to 500 under different operating conditions are compared with two well-known algorithms in the literature and demonstrate the effectiveness of the proposed algorithm.

      PubDate: 2016-03-12T16:17:45Z
  • A Hierarchical Heterogeneous Ant Colony Optimization based approach for
           efficient Action Rule mining
    • 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
  • Application of hybrid heuristic optimization algorithms for solving
           optimal regional dispatch of energy and reserve considering the social
           welfare of the participating markets
    • Abstract: Publication date: Available online 2 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Marlon S. Chamba, Osvaldo Añó, Rodolfo Reta
      Market integration allows increasing the social welfare of a given society. In most markets, integration also raises the social welfare of the participating markets (partakers). However, electricity markets have complexities such as transmission network congestion and requirements of power reserve that could lead to a decrease in the social welfare of some partakers. The social welfare reduction of partakers, if it occurs, would surely be a hindrance to the development of regional markets, since participants are usually national systems. This paper shows a new model for the regional dispatch of energy and reserve, and proposes as constraints that the social welfare of partakers does not decrease with respect to that obtained from the isolated optimal operation. These social welfare constraints are characterized by their stochastic nature and their dependence on the energy price of different operating states. The problem is solved by the combination of two optimization models (hybrid optimization): A linear model embedded within a meta-heuristic algorithm, which is known as the swarm version of the Means Variance Mapping Optimization (MVMOS). MVMOS allows incorporating the stochastic nature of social welfare constraints through a dynamic penalty scheme, which considers the fulfillment degree along with the dynamics of the search process.

      PubDate: 2016-03-07T16:06:16Z
  • Microarray medical data classification using kernel ridge regression and
           modified cat swarm optimization based gene selection system
    • Abstract: Publication date: Available online 2 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): P. Mohapatra, S. Chakravarty, P.K. Dash
      Microarray gene expression based medical data classification has remained as one of the most challenging research areas in the field of bioinformatics, machine learning and pattern classification. This paper proposes two variations of kernel ridge regression (KRR), namely wavelet kernel ridge regression (WKRR) and radial basis kernel ridge regression (RKRR) for classification of microarray medical datasets. Microarray medical datasets contain irrelevant and redundant genes which cause high number of gene expression i.e. dimensionality and small sample sizes. To overcome the curse of dimensionality of the microarray datasets, modified cat swarm optimization (MCSO), a naturally inspired evolutionary algorithm, is used to select the most relevant features from the datasets. The adequacies of the classifiers are demonstrated by employing four from each binary and multi-class microarray medical datasets. Breast cancer, prostate cancer, colon tumor, leukemia datasets belong to the former and leukemia1, leukemia2, SRBCT, brain tumor1 to the latter. A number of useful performance evaluation measures including accuracy, sensitivity, specificity, confusion matrix, Gmean, F-score and the area under the receiver operating characteristic (ROC) curve are considered to examine the efficacy of the model. Other models like simple ridge regression (RR), online sequential ridge regression (OSRR), support vector machine radial basis function (SVMRBF), support vector machine polynomial (SVMPoly) and random forest are studied and analyzed for comparison. The experimental results demonstrate that KRR outperforms other models irrespective of the datasets and WKRR produces better results as compared to RKRR. Finally, when the results are compared on the basis of binary and multi-class datasets, it is found that binary class yields a little bit better result as compared to the multiclass irrespective of models.

      PubDate: 2016-03-07T16:06:16Z
  • Ageist Spider Monkey Optimization Algorithm
    • 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
  • Directionally Driven Self-Regulating Particle Swarm Optimization algorithm
    • Abstract: Publication date: Available online 4 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): M.R. Tanweer, R. Auditya, S. Suresh, N. Sundararajan, N. Srikanth
      In this paper, an improved variant of the Self-Regulating Particle Swarm Optimization (SRPSO) algorithm is proposed that further enhances the performance of the basic SRPSO algorithm and is referred to as a Directionally Driven Self-Regulating Particle Swarm Optimization (DD-SRPSO) algorithm. In DD-SRPSO, we incorporate two new strategies, namely, a directional update strategy and a rotational invariant strategy. As in SRPSO, the best particle in DD-SRPSO uses the same self-regulated inertia weight update strategy. The poorly performing particles are grouped together to get directional updates from the group of elite particles. All the remaining particles are randomly selected to undergo either the SRPSO strategy of self-perception of the global search direction or the rotational invariant strategy to explore the rotation variance property of the search space. The performance of the DD-SRPSO algorithm is evaluated using the complex, shifted and rotated benchmark functions from CEC2013. These results are compared with seven current state-of-the-art PSO variants like Social Learning PSO (SLPSO), Comprehensive Learning PSO (CLPSO), SRPSO, etc. The results clearly indicate that the proposed learning strategies have significantly enhanced the performance of the basic SRPSO algorithm. The performance has also been compared with state-of-the-art evolutionary algorithms like Mean Variance Mapping Optimization (MVMO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) on the recently proposed numerically expensive optimzation CEC2015 benchmark functions whereby DD-SRPSO has provided competitive solutions. The results also indicate that the DD-SRPSO algorithm achieves a faster convergence and provides better solutions in a diverse set of problems with a 95% confidence level, thereby promising to be an effective optimization algorithm for real-world applications.

      PubDate: 2016-02-12T13:16:26Z
  • Recent advances in differential evolution – An updated survey
    • Abstract: Publication date: Available online 1 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Swagatam Das, Sankha Subhra Mullick, P.N. Suganthan
      Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

      PubDate: 2016-02-12T13:16:26Z
  • Metaheuristics in structural optimization and discussions on harmony
           search algorithm
    • Abstract: Publication date: Available online 2 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): M.P. Saka, O. Hasançebi, Z.W. Geem
      Metaheuristic algorithms have provided efficient tools to engineering designers by which it became possible to determine the optimum solutions of engineering design optimization problems encountered in every day practice. Generally metaheuristics are based on metaphors that are taken from nature or some other processes. Because of their success of providing solutions to complex engineering design optimization problems the recent literature has flourished with a large number of new metaheuristics based on a variety of metaphors. Despite the fact that most of these techniques have numerically proven themselves as reliable and strong tools for solutions of design optimization problems in many different disciplines, some argue against these methods on account of not having mathematical background and making use of irrelevant and odd metaphors. However, so long as these efforts bring about computationally efficient and robust optimum structural tools for designers what type of metaphors they are based on becomes insignificant. After a brief historical review of structural optimization this article opens this issue up for discussion of the readers and attempts to answer some of the criticisms asserted in some recent publications related with the novelty of metaheuristics.

      PubDate: 2016-02-12T13:16:26Z
  • Prediction based mean-variance model for constrained portfolio assets
           selection using multiobjective evolutionary algorithms
    • Abstract: Publication date: Available online 1 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Sudhansu Kumar Mishra, Ganapati Panda, Babita Majhi
      In this paper, a novel prediction based mean-variance (PBMV) model has been proposed, as an alternative to the conventional Markowitz mean-variance model, to solve the constrained portfolio optimization problem. In the Markowitz mean-variance model, the expected future return is taken as the mean of the past returns, which is incorrect. In the proposed model, first the expected future returns are predicted, using a low complexity heuristic functional link artificial neural network (HFLANN) model and the portfolio optimization task is carried out by using multi-objective evolutionary algorithms (MOEAs). In this paper, swarm intelligence based, multiobjective optimization algorithm, namely self-regulating multiobjective particle swarm optimization (SR-MOPSO) has also been proposed and employed efficiently to solve this important problem. The Pareto solutions obtained by applying two other competitive MOEAs and using the proposed PBMV models and Markowitz mean-variance model have been compared, considering six performance metrics and the Pareto fronts. Moreover, in the present study, the nonparametric statistical analysis using the Sign test and Wilcoxon rank test are also carried out, to compare the performance of the algorithms pair wise. It is observed that, the proposed PBMV model based approach provides better Pareto solutions, maintaining adequate diversity, and also quite comparable to the Markowitz model. From the simulation result, it is observed that the self regulating multiobjective particle swarm optimization (SR-MOPSO) algorithm based on PBMV model, provides the best Pareto solutions amongst those offered by other MOEAs.

      PubDate: 2016-02-12T13:16:26Z
  • A multilevel ACO approach for solving forest transportation planning
           problems with environmental constraints
    • 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
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: February 2016
      Source:Swarm and Evolutionary Computation, Volume 26

      PubDate: 2016-01-26T07:27:39Z
  • Solving Multi-objective Portfolio Optimization Problem Using Invasive Weed
    • 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
  • Particle swarm and Box’s complex optimization methods to design
           linear tubular switched reluctance generators for wave energy conversion
    • 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
  • D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving
           Word Sense Disambiguation
    • Abstract: Publication date: Available online 9 January 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Sallam Abualhaija, Karl-Heinz Zimmermann
      Word sense disambiguation is an early problem in the field of computational linguistics, and is defined as identifying the sense (or senses) that most likely represents a word, or a sequence of words in a given context. Word sense disambiguation was recently addressed as a combinatorial optimization problem in which the goal is to find a sequence of senses that maximizes the semantic relatedness among the target words. In this article, we propose a novel algorithm for solving the word sense disambiguation problem, namely D-Bees, that is inspired by the bee colony optimization meta-heuristic in which several artificial bee agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a standard SemEval 2007 task 7 coarse-grained English all-words corpus and is compared to the genetic and simulated annealing algorithms as well as an ant colony algorithm. It will follow that the bee and ant colony optimization approaches perform on par achieving better results than the genetic and simulated annealing algorithms on the given dataset.

      PubDate: 2016-01-11T05:12:06Z
  • Structural damage detection using artificial bee colony algorithm with
           hybrid search strategy
    • 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
  • A hybridization of an Improved Particle Swarm optimization and
           Gravitational Search Algorithm for Multi-Robot Path Planning
    • 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
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2015