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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  [2805 journals]
  • 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
  • Genetic Algorithm optimised Chemical reactors network: A novel technique
           for alternative fuels emission prediction
    • Abstract: Publication date: Available online 17 December 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Christopher C. Leong, Simon Blakey, Christopher W. Wilson
      Sustainability of the conventional jet fuels and climate change has attracted the aviation sector to diversity to alternative fuels. However, fuel diversification requires an assessment of the long term impact to engine performance and engine emissions through the combustion process, as alternative fuels are not as well understood as conventional jet fuel. A detailed experimental study on alternative fuels emissions across the entire aircraft fleet is impractical. Therefore a plausible method of computer modelling combined Genetic algorithm and Chemical reactors network was developed to predict alternative fuels gaseous emissions, namely, Carbon Monoxide, Nitrogen Oxides and Unburned Hydrocarbons in aircraft engines. To evaluate the feasibility and accuracy of the technique, exhaust emission measurements were performed on a re-commissioned Artouste Mk113 Auxiliary Power Unit, located at the University of Sheffield's Low Carbon Combustion Centre. The simulation produced results with good agreements with the experimental data. The optimised model was used to extrapolate emissions data from different blends of alternative fuels that did not operate during the campaign. The proposed technique showed that it can develop a data base of alternative fuels emissions and also act as a guideline for alternative fuels development.

      PubDate: 2015-12-22T03:19:07Z
  • A HPSO for solving dynamic and discrete berth allocation problem and
           dynamic quay crane assignment problem simultaneously
    • Abstract: Publication date: Available online 2 December 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Hsien-Pin Hsu
      Berth allocation problem (BAP) and quay crane assignment problem (QCAP) are two essential seaside operations planning problems faced by operational planners of a container terminal. The two planning problems have been often solved by genetic algorithms (GAs) separately or simultaneously. However, almost all these GAs can only support time-invariant QC assignment in which the number of QCs assigned to a ship is unchanged. In this study a hybrid particle swarm optimization (HPSO), combining an improved PSO with an event-based heuristic, is proposed to deal with two specific seaside operations planning problems, the dynamic and discrete BAP (DDBAP) and the dynamic QCAP (DQCAP). In the HPSO, the improved PSO first generates a DDBAP solution and a DQCAP solution with time-invariant QC assignment. Then, the event-based heuristic transforms the DQCAP solution into one with variable-in-time QC assignment in which the number of QCs assigned to a ship can be further changed. To investigate its effeteness, the HPSO has been compared to a GA (namely GA1) with time-invariant QC assignment and a hybrid GA (HGA) with variable-in-time QC assignment. Experimental results show that the HPSO outperforms the HGA and GA1 in terms of fitness value (FV).

      PubDate: 2015-12-06T19:34:29Z
    • Abstract: Publication date: Available online 4 December 2015
      Source:Swarm and Evolutionary Computation
      Author(s): G. Naresh, M. Ramalinga Raju, S.V.L. Narasimham
      Power System Stabilizers (PSS) are generally employed to damp electromechanical oscillations by providing auxiliary stabilizing signals to the excitation system of the generators. But it has been found that these Conventional PSS (CPSS) do not provide sufficient damping for inter-area oscillations in multi-machine power systems. Thyristor Controlled Series Capacitor (TCSC) has immense potential in damping of inter-area power swings and in mitigating the sub-synchronous resonance. In this paper Improved Harmony Search Algorithm (IHSA) has been proposed for coordinated design of multiple PSS and TCSC in order to effectively damp the oscillations. The results obtained by using IHSA on WSCC 3-machine, 9-bus system are found to be superior compared to the results obtained using Bacterial Swarm Optimization (BSO) algorithm. The damping performance of conventional PSS and TCSC controllers is also compared with coordinated design of IHSA based PSS and TCSC on New England 10-machine, 39-bus system over wide range of operating conditions and contingencies. To demonstrate the effectiveness of the proposed technique the results obtained on this test system are also compared with the results obtained with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search Algorithm (HSA) and Bacterial Swarm Optimization (BSO).

      PubDate: 2015-12-06T19:34:29Z
  • A distributed neuro-genetic programming tool
    • Abstract: Publication date: Available online 23 November 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Marco Russo
      This paper describes the performance of the Brain Project, a distributed software tool for the formal modeling of numerical data using a hybrid neural-genetic programming technique. One of the most interesting characteristics of the Brain Project is its distributed implementation. Unlike many other parallel and/or distributed solutions the only requirement of the Brain Project is that the collaborating personal computers must be 64-bit Linux machines connected to Internet via the transmission control protocol/internet protocol. The performance of the Brain Project is clearly enhanced with the very simple parallelization scheme illustrated in the paper. Although the Brain Project presents many innovative solutions for the genetic programming research, this paper focuses mainly on its behavior in the distributed environment.

      PubDate: 2015-11-26T18:23:17Z
  • Benchmarking NLopt and state-of-the-art algorithms for Continuous Global
           Optimization via IACOR
    • Abstract: Publication date: Available online 22 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Udit Kumar, Sumit Soman, Jayadeva
      This paper presents a comparative analysis of the performance of the Incremental Ant Colony algorithm for continuous optimization ( IACO R ), with different algorithms provided in the NLopt library. The key objective is to understand how various algorithms in the NLopt library perform in combination with the Multi Trajectory Local Search (Mtsls1) technique. A hybrid approach has been introduced for the local search strategy, by the use of a parameter that allows for probabilistic selection between Mtsls1 and the NLopt algorithm. In case of stagnation, a switch is made based on the algorithm being used in the previous iteration. This paper presents an exhaustive comparison on the performance of these approaches on Soft Computing (SOCO) and Congress on Evolutionary Computation (CEC) 2014 benchmarks. For both sets of benchmarks, we conclude that the best performing algorithm is a hybrid variant of Mtsls1 with BFGS for local search.

      PubDate: 2015-10-27T18:25:25Z
  • Pairwise independence and its impact on Estimation of Distribution
    • Abstract: Publication date: Available online 22 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Jean P. Martins, Alexandre C.B. Delbem
      Estimation of Distribution Algorithms (EDAs) were proposed as an alternative for traditional evolutionary algorithms in which reproduction operators could rely on information extracted from the population to enable a more effective search. Since information is usually represented as a probabilistic graphic model, the effectiveness of EDAs strongly depends on how accurately such models represent the population. In this sense, models of increasing complexity have been employed by EDAs, with the most successful ones being able to encode multivariate factorizations of joint probability distributions. However, some studies have shown that even multivariate EDAs fail to build accurate models for problems in which there is an intrinsic pairwise independence between variables. This study elucidates how pairwise independence impacts the linkage learning procedures of multivariate EDAs and affects their accuracy. First, the necessary conditions for learning additively separable functions are assessed, from which it is shown that extreme multimodality can induce pairwise independence. Second, it is demonstrated that in the presence of pairwise independence the approximate linkage learning procedures employed by many EDAs are not able to retrieve high-order dependences. Finally, in an attempt to infer how likely pairwise independence occur in practical problems, the case of non-separable functions is empirically investigated. For this purpose, the NK-model and the Linkage-Tree Genetic Algorithm (LTGA) were used as a study case and a range of usefulness for the LTGA was estimated according to N (problem size) and K (degree of interactions among variables and multimodality). The results indicated that LTGA linkage learning is probably more useful for K ≤ 6 on instances with random linkages (this range grows with N), and for K ≤ 9 on instances with nearest-neighbor linkages (this range is stable with N). Outside these ranges, pairwise independence is more likely to occur, which deteriorates models accuracy and impairs LTGA performance.

      PubDate: 2015-10-27T18:25:25Z
  • Investigations of a GPU-based Levy-Firefly Algorithm for Constrained
           Optimization of Radiation Therapy Treatment Planning
    • Abstract: Publication date: Available online 26 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Georgios Kalantzis, Charles Shang, Yu Lei, Theodora Leventouri
      Intensity modulated radiation therapy (IMRT) affords the potential to decrease radiation therapy associated toxicity by creating highly conformal dose distribution to tumor. Inverse optimization of IMRT treatment plans is often a time intensive task due to the large scale solution space, and the indubitably complexity of the task. Furthermore, the incorporation of conflicting dose constraints in the treatment plan, usually introduces an additional degree of intricacy. Metaheuristic algorithms have been proposed in the past for global optimization in IMRT treatment planning. However one disadvantage of the aforementioned methods is their extensive computational cost. One way to ameliorate their performance deficiency is to parallelize the application. In the current study we propose a GPU-based Levy-Firefly algorithm (LFA) for constrained optimization of IMRT treatment planning. The evaluation of our method was realized for two treatment cases: a prostate and a head and neck (H&N) cancer IMRT plans. The studies indicated an ascendable increase of the speedup factor as a function of the number of pencil beams with a maximum of ~11, whereas the performance of the algorithm was decreasing as a function of the population of the swarm particles. In addition, from our simulation results we concluded that 200 fireflies were sufficient for the algorithm to converge in less than 80 iterations. Finally, we demonstrated the effect of penalizing factors on constraining the maximum dose at the organs at risk (OAR) by impeding the dose coverage of the tumor target. The impetus behind our study was to elucidate the performance and generic attributes of the proposed algorithm, as well as the potential of its applicability for IMRT optimization problems.

      PubDate: 2015-10-27T18:25:25Z
  • An efficient two-level swarm intelligence approach for RNA secondary
           structure prediction with bi-objective minimum free energy scores
    • Abstract: Publication date: Available online 19 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Soniya Lalwani, Rajesh Kumar, Nilama Gupta
      This paper introduces a set-based two-level particle swarm optimization algorithm (TL-PSOfold) with multiple swarms for finding secondary structure of RNA with prediction accuracy. First objective is concerned with maximizing number of stacked loops at hydrogen bond, whereas, second objective deals with minimum free energy (MFE) at standard nearest neighbor database (NNDB). First level of the algorithm works on the entire search space for the best solution of each swarm, whereas, the second level works at the gbest solution of each swarm. The set based PSO approach has been applied at both levels to represent and update the set of ordered pairs of the folded RNA sequence. Improved weight parameter schemes with mutation operators are implemented for better convergence and to overcome the stagnation problem. Bi-objectives nature of TL-PSOfold enables the algorithm to achieve maximum matching pairs as well as optimum structure at respective levels. The performance of TL-PSOfold is compared with a family of PSO based algorithms i.e. HelixPSO v1, HelixPSO v2, PSOfold, SetPSO, IPSO, FPSO, popular secondary structure prediction software RNAfold, mfold and other metaheuristics RNAPredict, SARNA-Predict at the criteria of sensitivity, specificity and F-measure. Simulation results for TL-PSOfold show that it yields higher prediction accuracy than all the compared approaches. The claim is supported by the non-parametric statistical significance testing using kruskal-wallis test followed by post-hoc analysis.

      PubDate: 2015-10-22T18:16:57Z
  • Performance of Laplacian Biogeography-Based Optimization Algorithm on CEC
           2014 continuous optimization benchmarks and Camera Calibration Problem
    • Abstract: Publication date: Available online 17 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Vanita Garg, Kusum Deep
      This paper provides three innovations. Firstly, a new Laplacian BBO is presented which introduces a Laplacian migration operator based on the Laplace Crossover of Real Coded Genetic Algorithms. Secondly, the performance of the Laplacian BBO and Blended BBO is exhibited on the latest benchmark collection of CEC 2014. (To the best of the knowledge of the authors, the complete CEC 2014 benchmarks have not been solved by Blended BBO). On the basis of the criteria laid down in CEC 2014 as well as popular evaluation criteria called Performance Index, It is shown that Laplacian BBO outperforms Blended BBO in terms of error value defined in CEC 2014 benchmark collection. T-Test has also been employed to strengthen the fact that Laplacian BBO performs better than Blended BBO. The third innovation of the paper is the use of the proposed Laplacian BBO and Blended BBO to solve a real life problem from the field of Computer Vision. It is concluded that proposed Laplacian BBO is an efficient and reliable algorithm for solving not only the continuous functions but also real life problems like camera calibration.

      PubDate: 2015-10-22T18:16:57Z
  • Load frequency control of interconnected power system using grey wolf
    • Abstract: Publication date: Available online 19 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Dipayan Guha, Provas Kumar Roy, Subrata Banerjee
      In this article an attempt has been made to solve load frequency control (LFC) problem in an interconnected power system network equipped with classical PI/PID controller using gray wolf optimization (GWO) technique. Initially, proposed algorithm is used for two-area interconnected non-reheat thermal-thermal power system and then the study is extended to three other realistic power systems, viz. (i) two-area multi-units hydro-thermal, (ii) two-area multi-sources power system having thermal, hydro and gas power plants and (iii) three-unequal-area all thermal power system for better validation of the effectiveness of proposed algorithm. The generation rate constraint (GRC) of the steam turbine is included in the system modeling and dynamic stability of aforesaid systems is investigated in the presence of GRC. The controller gains are optimized by using GWO algorithm employing integral time multiplied absolute error (ITAE) based fitness function. Performance of the proposed GWO algorithm has been compared with comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE) and other similar meta-heuristic optimization techniques available in literature for similar test system. Moreover, to demonstrate the robustness of proposed GWO algorithm, sensitivity analysis is performed by varying the operating loading conditions and system parameters in the range of ± 50 % . Simulation results show that GWO has better tuning capability than CLPSO, EPSDE and other similar population-based optimization techniques.

      PubDate: 2015-10-22T18:16:57Z
  • Artificial Infectious Disease Optimization: A SEIQR Epidemic Dynamic
           Model-based Function Optimization Algorithm
    • Abstract: Publication date: Available online 9 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Guang-qiu Huang
      To solve some complicated function optimization problems, an artificial infectious disease optimization algorithm based on the SEIQR epidemic model is constructed, it is called as the SEIQR algorithm, or SEIQRA in short. The algorithm supposes that some human individuals exist in an ecosystem; each individual is characterized by a number of features; an infectious disease (SARS) exists in the ecosystem and spreads among individuals, the disease attacks only a part of features of an individual. Each infected individual may pass through such states as susceptibility (S), exposure (E), infection (I), quarantine (Q) and recovery (R). State S, E, I, Q and R can automatically and dynamically divide all people in the ecosystem into five classes, it provides the diversity for SEIQRA; that people can be attacked by the infectious disease and then transfer it to other people can cause information exchange among people, information exchange can make a person to transit from one state to another; state transitions can be transformed into operators of SEIQRA; the algorithm has 13 legal state transitions, which corresponds to 13 operators; the transmission rules of the infectious disease among people is just the logic to control state transitions of individuals among S, E, I, Q and R, it is just the synergy of SEIQRA, the synergy can be transformed into the logic structure of the algorithm. The 13 operators in the algorithm provide a native opportunity to integrate many operations with different purposes; these operations include average, differential, expansion, chevy, reflection and crossover. The 13 operators are executed equi-probably; a stable heart rhythm of the algorithm is realized. Because the infectious disease can only attack a small part of organs of a person when it spreads among people, the part variables iteration strategy (PVI) can be ingeniously applied, thus enabling the algorithm to possess of high performance of computation, high suitability for solving some kinds of complicated optimization problems, especially high dimensional optimization problems. Results show that SEIQRA has characteristics of strong search capability and global convergence, and has a high convergence speed for some complicated functions optimization problems.

      PubDate: 2015-10-12T16:52:44Z
  • Parallel Improved Quantum Inspired Evolutionary Algorithm to solve Large
           Size Quadratic Knapsack Problems
    • Abstract: Publication date: Available online 3 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): C. Patvardhan, Sulabh Bansal, A. Srivastav
      Quadratic Knapsack Problem (QKP), an extension of the canonical simple Knapsack Problem, is NP Hard in the stronger sense. No pseudo-polynomial time algorithm is known to exist which can solve QKP instances. QKP has been studied intensively due to its simple structure yet challenging difficulty and numerous applications. A few attempts have been made to solve large size instances of QKP due to its complexity. Quantum Inspired Evolutionary Algorithm (QIEA) provides a generic framework that has often been carefully tailored for a given problem to obtain an effective implementation. In this work, an improved and parallelized QIEA, dubbed IQIEA-P is presented. Several additional features make it more balanced in exploration and exploitation and thus have better applicability. Computational experiments are presented on large QKP instances of 1000 and 2000 items. The improvements are inherently parallelizable and, therefore, good speedups are obtained on a multi-core machine. No parallel algorithm is available for QKP. The solutions provided by QIEA-P are competitive with those obtained from the state of the art algorithm.

      PubDate: 2015-10-07T12:02:46Z
  • Unknown environment exploration of multi-robot system with the FORDPSO
    • Abstract: Publication date: Available online 1 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Dongshu Wang, Haitao Wang, Lei Liu
      Effective environment exploration in unknown environment is precondition of constructing the environment map and carrying out other tasks for multi-robot system. Due to its excellent performance, particle swarm optimization (PSO) has been widely used in multi-robot exploration field. To deal with its drawback – easily trapped in local optima, Darwinian PSO (DPSO) optimization is proposed by Tillett [1] with the natural selection function and first used in real world robot exploration by Couceiro [2], forming the robotic DPSO (RDPSO). To increase the algorithm performance and control its convergence rate, fractional calculus is used to replace inertia component in RDPSO for its “memory” ability and forming the fractional order RDPSO (FORDPSO). This paper presents a formal analysis of RDPSO and studies the influence of the coefficients on FORDPSO algorithm. To satisfy the requirement of dynamically changing robots' behaviours during the exploration, fuzzy inferring system is designed to achieve better control coefficients. Experiment results obtained in two complex simulated environments illustrate that biological and siciological inspiration is effective to meet the challenges of multi-robot system application in unknown environment exploration, and the exploration effect of the fuzzy adaptive FORDPSO is better than that of the fixed coefficient FORDPSO. Furthermore, the performance of FORDPSO with different neighbourhood topologies are studied and compared with other six PSO variations. All the results demonstrate the effect of the FORDPSO on the multi-robot environment exploration.

      PubDate: 2015-10-02T10:53:57Z
  • Improving the Performance of Evolutionary Multi-objective Co-clustering
           Models for Community Detection in Complex Social Networks
    • Abstract: Publication date: Available online 28 September 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Bara'a A. Attea, Wisam A. Hariz, Mayyadah F. Abdulhalim
      Due to globalization, the characteristic of many systems in biology, engineering and sociology paradigms can nowadays be captured and investigated as networks of connected communities. Detecting natural divisions in such complex networks is proved to be extremely NP-hard problem that recently enjoyed a considerable interest. Among the proposed methods, the field of multi-objective evolutionary algorithms (MOEAs) reveals outperformed results. Despite the existing efforts on designing effective multi-objective optimization (MOO) models and investigating the performance of several MOEAs for detecting natural community structures, their techniques lack the introduction of some problem-specific heuristic operators that realize their principles from the natural structure of communities. Moreover, most of these MOEAs evaluate and compare their performance under different algorithmic settings that may hold unmerited conclusions. The main contribution of this paper is two-fold. Firstly, to reformulate the community detection problem as a MOO model that can simultaneously capture the intra- and inter-community structures. Secondly, to propose a heuristic perturbation operator that can emphasize the search for such intra- and inter-community connections in an attempt to offer a positive collaboration with the MOO model. One of the prominent multi-objective evolutionary algorithms (the so-called MOEA/D) is adopted with the proposed community detection model and the perturbation operator to identify the overlapped community sets in complex networks. Under the same MOEA/D characteristic settings, the performance of the proposed model and test results are evaluated against three state-of-the-art MOO models. The experiments on real-world and synthetic social networks of different complexities demonstrate the effectiveness of the proposed model to define community detection problem. Moreover, the results prove the positive impact of the proposed heuristic operator to harness the strength of all MOO models in both terms of convergence velocity and convergence reliability.

      PubDate: 2015-10-02T10:53:57Z
  • Evolutionary optimization technique for comparative analysis of different
           classical controllers for an isolated wind-diesel hybrid power system
    • Abstract: Publication date: Available online 24 September 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Tarkeshwar, V. Mukherjee
      In this paper, the considered hybrid power system (HPS) is having a wind turbine generator, a diesel engine generator and a storage device (such as capacitive energy storage). This paper presents a comparative study of frequency and power control for the studied isolated wind-diesel HPS with four different classical controllers for the pitch control of wind turbines and the speed governor control of diesel engine generator. The classical controllers considered are integral, proportional-integral, integral-derivative and proportional-integral-derivative (PID) controller. A quasi-oppositional harmony search (QOHS) algorithm is proposed for the tuning of the controller gains. The comparative dynamic simulation response results indicate that better performance may be achieved with choosing PID controller among the considered classical controllers when subjected to different perturbation. Stability and sensitivity analysis reveals that the optimized PID controller gains offered by the proposed QOHS algorithm are quite robust and need not be reset for wide changes in system perturbations.

      PubDate: 2015-09-27T16:59:49Z
  • Opposition-Based Magnetic Optimization Algorithm With Parameter Adaptation
    • Abstract: Publication date: Available online 16 September 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Mahdi Aziz, Mohammad - H. Tayarani- N.
      Magnetic Optimization Algorithm (MOA) has emerged as a promising optimization algorithm that is inspired by the principles of magnetic field theory. In this paper we improve the performance of the algorithm in two aspects. First an Opposition-Based Learning (OBL) approach is proposed for the algorithm which is applied to the movement operator of the algorithm. Second, by learning from the algorithm's past experience, an adaptive parameter control strategy which dynamically sets the parameters of the algorithm during the optimization is proposed. To show the significance of the proposed parameter adaptation strategy, we compare the algorithm with two well-known parameter setting techniques on a number of benchmark problems. The results indicate that although the proposed algorithm with the adaptation strategy does not require to set the parameters of the algorithm prior to the optimization process, it outperforms MOA with other parameter setting strategies in most large-scale optimization problems. We also study the algorithm while employing the OBL by comparing it with the original version of MOA. Furthermore, the proposed algorithm is tested and compared with seven traditional population-based algorithms and eight state-of-the-art optimization algorithms. The comparisons demonstrate that the proposed algorithm outperforms the traditional algorithms in most benchmark problems, and its results is comparative to those obtained by the state-of-the-art algorithms.

      PubDate: 2015-09-18T09:37:38Z
  • A Study of the Classical Differential Evolution Control Parameters
    • Abstract: Publication date: Available online 12 September 2015
      Source:Swarm and Evolutionary Computation
      Author(s): F. Peñuñuri, C. Cab, O. Carvente, M.A. Zambrano-Arjona, J.A. Tapia
      An extensive numerical study has been conducted to shed some light on the selection of parameters for the Classical Differential Evolution (DE/rand/1/bin) optimization method with the dither variant. It is well known that the crossover probability (Cr) has an active role in the convergence of the method. Our experiments show that even when the number of generations needed to achieve convergence as a function of the Cr parameter is of a stochastic nature, in some regions a reasonably well defined dependence of this number as a function of Crcan be observed. Motivated by this result, a self adaptive DE methodology has been proposed. This new methodology applies the DE/rand/1/bin strategy itself to find a good value for the Cr parameter. Regarding the population size parameter, a phenomenological study involving the search space, the tolerance error, and the complexity of the function has been made. The proposed methodology has been applied to ten of the most common test functions, giving the best success rate (100% in all the studied examples) and in general a faster convergence than the classical DE/rand/1/bin strategy.

      PubDate: 2015-09-13T09:31:04Z
  • Opposition-Based Learning In The Shuffled Bidirectional Differential
           Evolution Algorithm
    • Abstract: Publication date: Available online 2 September 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Morteza Alinia Ahandani
      The opposition-based learning (OBL) strategy by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. In this paper, the OBL is employed to speed up the shuffled bidirectional differential evolution (SBDE) algorithm. The SBDE by employing the partitioning, shuffling and bidirectional optimization concepts increases the number and diversity of search moves in respect to the original differential evolution (DE). So with incorporating the SBDE and OBL strategy, we can obtain the algorithms with an ability of better exploring the promising areas of search space without occurring stagnation or premature convergence. Experiments on 25 benchmark functions and non-parametric analysis of obtained results demonstrate a better performance of our proposed algorithms than original SBDE algorithm. Also an extensive performance comparison the proposed algorithms with some modern and state-of-the-art DE algorithms reported in the literature confirms a statistically significantly better performance of proposed algorithms in most cases. In a later part of the comparative experiments, firstly proposed algorithms are compared with other evolutionary algorithms (EAs) proposed for special session CEC2005. Then a comparison against a wide variety of recently proposed EAs is performed. The obtained results show that in most cases the proposed algorithms have a statistically significantly better performance in comparable to several existing EAs. Keywords

      PubDate: 2015-09-03T09:21:59Z
  • Discrete Honeybee Mating Optimization Algorithm for the Routing of
           Battery-operated Automated Guidance Electric Vehicles in Personal Rapid
           Transit Systems
    • Abstract: Publication date: Available online 20 August 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Ezzeddine Fatnassi, Olfa Chebbi, Jouhaina Chaouachi
      Reducing the amount of energy consumed by mass transit systems can be a challenging task. The present study focuses on minimizing the energy consumed by a relatively new transportation system called a personal rapid transit (PRT) system. PRT systems provide automated direct nonstop transit services to their users. This study explores the routing problem associated with PRT where the aim is to minimize the energy consumption while considering the battery capacity of PRT vehicles. The honeybee mating optimization algorithm is adapted to this problem. A specific enhancement procedure is proposed that boosts the performance of the algorithm based on a specific initialization of the population. A multiple-descendant honeybee mating optimization algorithm is also proposed. Finally, the algorithms were verified using a set of 1320 randomly generated instances and extensive statistical analyses were performed to validate the results obtained.

      PubDate: 2015-08-23T01:53:27Z
  • Towards power plant output modeling and optimization using parallel
           Regression Random Forest
    • Abstract: Publication date: Available online 14 August 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Jan Janoušek, Petr Gajdoš, Pavel Dohnálek, Michal Radecký
      In this paper, we explore the possibilities of using the Random Forest algorithm in its regression version to predict the power output of a power plant based on hourly measured data. This is a task commonly leading to a optimization problem that is, in general, best solved using a bio-inspired technique. We extend the results already published on this topic and show that Regression Random Forest can be a better alternative to solve the problem. A comparison of the method with previously published results is included. In order to implement the algorithm in a way that is as efficient as possible, a massively parallel implementation using a Graphics Processing Unit was used and is also described.

      PubDate: 2015-08-14T05:56:36Z
  • A parallel fruchterman-reingold algorithm optimized for fast visualization
           of large graphs and swarms of data
    • Abstract: Publication date: Available online 14 August 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Petr Gajdoš, Tomáš Ježowicz, Vojtěch Uher, Pavel Dohnálek
      Graphs in computer science are widely used in social network analysis, computer networks, transportation networks, and many other areas. In general, they can visualize relationships between objects. However, fast drawing of graphs and other structures containing large numbers of data points with readable layouts is still a challenge. This paper describes a novel variant of the Fruchterman-Reingold graph layout algorithm which is adapted to GPU parallel architecture. A new approach based on space-filling curves and a new way of repulsive forces computation on GPU is described. The paper contains both performance and quality tests of the new algorithm.

      PubDate: 2015-08-14T05:56:36Z
  • Novel Benchmark Functions for Continuous Multimodal Optimization with
           Comparative Results
    • Abstract: Publication date: Available online 1 August 2015
      Source:Swarm and Evolutionary Computation
      Author(s): B.Y. Qu, J.J. Liang, Z.Y. Wang, Q. Chen, P.N. Suganthan
      Multi-modal optimization is concerned with locating multiple optima in one single run. Finding multiple solutions to a multi-modal optimization problem is especially useful in engineering, as the best solution may not always be the best realizable due to various practical constraints. To compare the performances of multi-modal optimization algorithms, multi-modal benchmark problems are always required. In this paper, 15 novel scalable multi-modal and real parameter benchmark problems are proposed. Among these 15 problems, 8 are extended simple functions while the rest are composition functions. These functions coordinate rotation and shift operations to create linkage among different dimensions and to place the optima at different locations, respectively. Four typical niching algorithms are used to solve the proposed problems. As shown by the experimental results, the proposed problems are challenging to these four recent algorithms.

      PubDate: 2015-08-03T03:29:02Z
  • Electromagnetic field optimization: A physics-inspired metaheuristic
           optimization algorithm
    • Abstract: Publication date: Available online 1 August 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Hosein Abedinpourshotorban, Siti Mariyam Shamsuddin, Zahra Beheshti, Dayang N.A. Jawawi
      — This paper presents a physics-inspired metaheuristic optimization algorithm, known as Electromagnetic Field Optimization (EFO). The proposed algorithm is inspired by the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, a possible solution is an electromagnetic particle made of electromagnets, and the number of electromagnets is determined by the number of variables of the optimization problem. EFO is a population-based algorithm in which the population is divided into three fields (positive, negative, and neutral); attraction-repulsion forces among electromagnets of these three fields lead particles toward global minima. The golden ratio determines the ratio between attraction and repulsion forces to help particles converge quickly and effectively. The experimental results on 30 high dimensional CEC 2014 benchmarks reflect the superiority of EFO in terms of accuracy and convergence speed over other state-of-the-art optimization algorithms.

      PubDate: 2015-08-03T03:29:02Z
  • Semantic Genetic Programming for Fast and Accurate Data Knowledge
    • Abstract: Publication date: Available online 26 July 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Mauro Castelli, Leonardo Vanneschi, Luca Manzoni, Aleš Popovič
      Big data knowledge discovery emerged as an important factor contributing to advancements in society at large. Still, researchers continuously seek to advance existing methods and provide novel ones for analysing vast data sets to make sense of the data, extract useful information, and build knowledge to inform decision making. In the last few years, a very promising variant of genetic programming was proposed: geometric semantic genetic programming. Its difference with the standard version of genetic programming consists in the fact that it uses new genetic operators, called geometric semantic operators, that, acting directly on the semantics of the candidate solutions, induce by definition a unimodal error surface on any supervised learning problem, independently from the complexity and size of the underlying data set. This property should improve the evolvability of genetic programming in presence of big data and thus makes geometric semantic genetic programming an extremely promising method for mining vast amounts of data. Nevertheless, to the best of our knowlegde, no contribution has appeared so far to employ this new technology to big data problems. This paper intends to fill this gap. For the first time, in fact, we show the effectiveness of geometric semantic genetic programming on several complex real-life problems, characterized by vast amounts of data, coming from several different application domains.

      PubDate: 2015-07-28T20:50:49Z
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