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Journal Cover Swarm and Evolutionary Computation
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   Hybrid Journal Hybrid journal (It can contain Open Access articles)
     ISSN (Print) 2210-6502
     Published by Elsevier Homepage  [2570 journals]   [SJR: 3.364]   [H-I: 8]
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: October 2014
      Source:Swarm and Evolutionary Computation, Volume 18




      PubDate: 2014-09-07T02:58:48Z
       
  • Metaheuristic Multi-objective optimization of constrained Futures
           Portfolios for effective risk management
    • Abstract: Publication date: Available online 27 August 2014
      Source:Swarm and Evolutionary Computation
      Author(s): G.A. Vijayalakshmi Pai , Thierry Michel
      In the Derivatives financial markets, Futures portfolios are perceived to be instruments of high risk, despite their flexibility of being used for portfolio protection (hedging) or for profitable trading (speculating). A multi-pronged approach for an effective management of the risks involved includes employing strategies such as, diversification between dissimilar markets, decision to go long or short on assets that make up the portfolio and risk tolerance or risk budgeting concerned with how risk is distributed across asset classes constituting the portfolio with all of these governed by investors’ preferences and capital budgets. However, the inclusion of such objectives and constraints turns the problem model complex for direct solving using analytical methods, inducing the need to look for metaheuristic solutions. In this paper, we present a metaheuristic solution to such a complex futures portfolio optimization problem, which strives to obtain an optimal well-diversified futures portfolio combining several asset classes such as equity indices, bonds and currencies, subject to the constraints of risk and capital budgets imposed on each of the asset classes, besides bounding constraints. The Herfindahl index function has been adopted to measure diversification of the long-short portfolio. In the absence of related work and considering the complexity of the problem that transforms it into a non linear multi-objective constrained optimization problem model, two metaheuristic strategies viz., Multi-objective Evolution Strategy and Multi-objective Differential Evolution, chosen from two different genres of Evolutionary Computation, have been employed to solve the complex problem and compare the results. Extensive simulations including performance analyses, convergence testing and back testing portfolio reliabilities have been undertaken to analyze the robustness of the optimization strategies.


      PubDate: 2014-09-02T02:20:47Z
       
  • PSO based placement of multiple wind DGs and Capacitors utilizing
           probabilistic load flow model
    • Abstract: Publication date: Available online 13 August 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Naveen Jain , S.N. Singh , S.C. Srivastava
      Wind Power Distributed Generators (WPDGs) are being increasingly placed in the power system due to their several technical and environmental benefits. In this paper, a modified Particle Swarm Optimizer (PSO) based method is proposed for placement of multiple WPDGs and capacitors. Monte Carlo Simulation (MCS) based probabilistic load flow, considering uncertainty in load demand and wind generation, is developed. It is used to modify the WPDGs’ and capacitors’ sizes utilizing a sensitivity based approach, which maintains branch currents and bus voltages within their prescribed limits. The proposed method is simple, accurate and generic, and it can provide multiple choices to the utilities to place capacitors and WPDGs under various system constraints. Results on three distribution networks demonstrate the effectiveness of the proposed method. The impact of the DG placement on the system voltage profile, line loss, environment, and cost of generation has also been investigated on three distribution systems.


      PubDate: 2014-08-16T00:45:41Z
       
  • A self adaptive differential harmony search based optimized extreme
           learning machine for financial time series prediction
    • Abstract: Publication date: Available online 6 August 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Rajashree Dash , P.K. Dash , Ranjeeta Bisoi
      This paper proposes a hybrid learning framework called Self Adaptive Differential Harmony Search Based Optimized Extreme Learning Machine (SADHS-OELM) for single hidden layer feed forward neural network (SLFN). The new learning paradigm seeks to take advantage of the generalization ability of extreme learning machines (ELM) along with the global learning capability of a self adaptive differential harmony search technique in order to optimize the fitting performance of SLFNs. SADHS is a variant of harmony search technique that uses the current to best mutation scheme of DE in the pitch adjustment operation for harmony improvisation process. SADHS has been used for optimal selection of the hidden layer parameters, the bias of neurons of the hidden-layer, and the regularization factor of robust least squares, whereas ELM has been applied to obtain the output weights analytically using a robust least squares solution. The proposed learning algorithm is applied on two SLFNs i.e. RBF and a low complexity Functional link Artificial Neural Networks (CEFLANN) for prediction of closing price and volatility of five different stock indices. The proposed learning scheme is also compared with other learning schemes like ELM, DE-OELM, DE, SADHS and two other variants of harmony search algorithm. Performance comparison of CEFLANN and RBF with different learning schemes clearly reveals that CEFLANN model trained with SADHS-OELM outperforms other learning methods and also the RBF model for both stock index and volatility prediction.


      PubDate: 2014-08-12T00:27:16Z
       
  • Differential evolution improved with self-adaptive control parameters
           based on simulated annealing
    • Abstract: Publication date: Available online 7 August 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Haixiang Guo , Yanan Li , Jinling Li , Han Sun , Deyun Wang
      Nowadays, differential evolution (DE) has attracted more and more attention as an effective approach for solving numerical optimization problems. However, the fact that users have to set the control parameters of DE according to every single different problem makes the adjustment of control parameters a very time-consuming work. To solve the problem, this paper presents an enhanced self-adaptive differential evolution (ESADE) for global numerical optimization over continuous space. In this ESADE algorithm, different control parameters have been used to make mutation and crossover. Here is the detailed process: Firstly, it initializes two groups of population. Secondly, it generates a set of control parameters for one of the two populations and then further creates another new series of control parameters for the other population through mutating the initial control parameters. Thirdly, once the control parameters are generated, the two populations are mutated and crossed to produce two groups of trial vectors. Finally, the target vectors are selected from the two groups of trial vectors by selecting operation. In order to enhance its global search capabilities, simulated annealing (SA) are involved in the selecting operation and the control parameters with better performance are chosen as the initial control parameters of the next generation. By employing a set of 17 benchmark functions from previous literature, this study carried out extensive computational simulations and comparisons and the computational results showed that the ESADE algorithm generally performed better than the state-of-the-art differential evolution variants and PSO. Besides, the influences of initialized ambient temperature and simulated annealing on the performance of ESADE have also been tested. For the purpose of testing the application of ESADE in solving real-world problems, ESADE was applied to identify the parameters of proton exchange membrane fuel cell model. The results showed that ESADE was equal with other state-of-the-art differential evolution variants on performance.


      PubDate: 2014-08-12T00:27:16Z
       
  • Hybrid ant optimization system for multiobjective economic emission load
           dispatch problem under fuzziness
    • Abstract: Publication date: Available online 5 July 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Abd Allah A. Mousa
      In this paper, a new hybrid optimization system is presented. Our approach integrates the merits of both ant colony optimization and steady state genetic algorithm and it has two characteristic features. Firstly, since there is instabilities in the global market and the rapid fluctuations of prices, a fuzzy representation of the economic emission load dispatch (EELD) problem has been defined, where the input data involve many parameters whose possible values may be assigned by the expert. Secondly, by enhancing ant colony optimization through steady state genetic algorithm, a strong robustness and more effectively algorithm was created. Also, stable Pareto set of solutions has been detected, where in a practical sense only Pareto optimal solutions that are stable are of interest since there are always uncertainties associated with efficiency data. Moreover to help the decision maker DM to extract the best compromise solution from a finite set of alternatives a Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method is adopted. It is based upon simultaneous minimization of distance from an ideal point (IP) and maximization of distance from a nadir point (NP). The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective EELD.


      PubDate: 2014-07-25T22:15:39Z
       
  • Comparative study of system on chip based solution for floating and fixed
           point differential evolution algorithm
    • Abstract: Publication date: Available online 8 July 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Rangababu Peesapati , Kiran Kumar Anumandla , Shravan Kudikala , Samrat L. Sabat
      This paper presents performance study of scalable hardware accelerator for fixed and floating point differential evolution (DE) algorithms in field programmable gate array (FPGA) using programmable system on chip (PSoC) approach. The hardware intellectual property (IP) of the DE is interfaced as a Slave Unit (SU) as well as an Auxiliary Processor Unit (APU) with the PowerPC440 processor based System on Chip (SoC) platform on Xilinx Virtex-5 FPGA. Six numerical benchmark functions are optimized to validate the IP and its interface to processor. From the experimental results, it is observed that (i) Both SU and APU interfaces of fixed and float DE IPs have shown similar acceleration because of less communication overhead. (ii) Floating point DE has higher resource utilization compared to fixed point DE. (iii) Both interfaces of fixed and float DE SoC systems have shown similar power consumption. (iii) Finally as a case study, an Infinite Impulse Response (IIR) based system identification task with second and fourth order plant transfer functions is implemented on PSoC using the fixed and float DE IP cores with fabric co-processor bus (FCB) interface using APU controller. The experimental results reveal that the acceleration factor and resources utilization increases with the increase in problem complexity.


      PubDate: 2014-07-25T22:15:39Z
       
  • Magnetic-inspired optimization algorithms: Operators and structures
    • Abstract: Publication date: Available online 11 July 2014
      Source:Swarm and Evolutionary Computation
      Author(s): M.-H. Tayarani-N. , M.-R. Akbarzadeh-T.
      A novel optimization algorithm, called the Magnetic Optimization Algorithms (MOAs), is proposed in this paper which is inspired by the principles of magnetic field theory. In MOA, the possible solutions are some magnetic particles scattered in the search space. In this respect, each magnetic particle has a measure of mass and magnetic field according to its fitness. In this scheme, the fitter magnetic particles are more massive, with stronger magnetic field. In terms of interaction, these particles are located in a structured population and apply a long range force of attraction to their neighbors. Ten different structures are proposed for the algorithm and the structure that offers the best performance is found. Also, to improve the exploration ability of the algorithm, several operators are proposed: a repulsive short-range force, an explosion operator, a combination of short-range force and explosion operator and a crossover interaction between the neighboring particles. In order to test the proposed algorithm and the proposed operators, the algorithm is compared with a variety of existing algorithms on 21 numerical benchmark functions. The experimental results suggest that the proposed algorithm outperforms some of the existing algorithms.


      PubDate: 2014-07-25T22:15:39Z
       
  • Using animal instincts to design efficient biomedical studies via particle
           swarm optimization
    • Abstract: Publication date: Available online 15 July 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Jiaheng Qiu , Ray-Bing Chen , Weichung Wang , Weng Kee Wong
      Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.


      PubDate: 2014-07-25T22:15:39Z
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: August 2014
      Source:Swarm and Evolutionary Computation, Volume 17




      PubDate: 2014-07-25T22:15:39Z
       
  • An efficient GA-PSO approach for solving mixed-integer nonlinear
           programming problem in reliability optimization
    • Abstract: Publication date: Available online 23 July 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Laxminarayan Sahoo , Avishek Banerjee , Asoke Kumar Bhunia , Samiran Chattopadhyay
      This paper deals with the development of an efficient hybrid approach based on genetic algorithm and particle swarm optimization for solving mixed integer nonlinear reliability optimization problems in series, series-parallel and bridge systems. This approach maximizes the overall system reliability subject to the nonlinear resource constraints arising on system cost, volume and weight. To meet these purposes, a novel hybrid algorithm with the features of advanced genetic algorithm and particle swarm optimization has been developed for determining the best found solutions. To test the capability and effectiveness of the proposed algorithm, three numerical examples have been solved and the computational results have been compared with the existing ones. From comparison, it is observed that the values of the system reliability are better than the existing results in all three examples. Moreover, the values of average computational time and standard deviation are better than the same of similar studies available in the existing literature. The proposed approach would be very helpful for reliability engineers/practitioners for better understanding about the system reliability and also to reach a better configuration.


      PubDate: 2014-07-25T22:15:39Z
       
  • Classification with cluster-based Bayesian multi-nets using Ant Colony
           Optimisation
    • Abstract: Publication date: Available online 14 May 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Khalid M. Salama , Alex A. Freitas
      Bayesian multi-net (BMN) classifiers consist of several local models, one for each data subset, to model asymmetric, more consistent dependency relationships among variables in each subset. This paper extends an earlier work of ours and proposes several contributions to the field of clustering-based BMN classifiers, using Ant Colony Optimisation (ACO). First, we introduce a new medoid-based method for ACO-based clustering in the Ant-ClustB MB algorithm to learn BMNs. Both this algorithm and our previously introduced Ant-ClustB IB for instance-based clustering have their effectiveness empirically compared in the context of the “cluster-then-learn” approach, in which the ACO clustering step completes before learning the local BN classifiers. Second, we propose a novel “cluster-with-learn” approach, in which the ACO meta-heuristic performs the clustering and the BMN learning in a synergistic fashion. Third, we adopt the latter approach in two new ACO algorithms: ACO-ClustB IB , using the instance-based method, and ACO-ClustB MB , using the medoid-based method. Empirical results are obtained on 30 UCI datasets.


      PubDate: 2014-07-25T22:15:39Z
       
  • mNAFSA: A novel approach for optimization in dynamic environments with
           global changes
    • Abstract: Publication date: Available online 29 May 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Danial Yazdani , Babak Nasiri , Alireza Sepas-Moghaddam , Mohammadreza Meybodi , Mohammadreza Akbarzadeh-Totonchi
      Artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligence algorithms that is widely used for optimization purposes in static environments. However, numerous real-world problems are dynamic and uncertain, which could not be solved using static approaches. The contribution of this paper is twofold. First, a novel AFSA algorithm, so called NAFSA, has been proposed in order to eliminate weak points of standard AFSA and increase convergence speed of the algorithm. Second, a multi-swarm algorithm based on NAFSA (mNAFSA) was presented to conquer particular challenges of dynamic environment by proposing several novel mechanisms including particularly modified multi-swarm mechanism for finding and covering potential optimum peaks and diversity increase mechanism which is applied after detecting an environment change. The proposed approaches have been evaluated on moving peak benchmark, which is the most prominent benchmark in this domain. This benchmark involves several parameters in order to simulate different configurations of dynamic environments. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of the tested dynamic environments modeled by moving peaks benchmark.


      PubDate: 2014-07-25T22:15:39Z
       
  • A hybrid particle swarm with a time-adaptive topology for constrained
           optimization
    • Abstract: Publication date: Available online 16 June 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Mohammad Reza Bonyadi , Xiang Li , Zbigniew Michalewicz
      For constrained optimization problems set in a continuous space, feasible regions might be disjointed and the optimal solution might be in any of these regions. Thus, locating these feasible regions (ideally all of them) as well as identifying the most promising region (in terms of objective value) at the end of the optimization process would be of a great significance. In this paper a time-adaptive topology is proposed that enables a variant of the particle swarm optimization (PSO) to locate many feasible regions at the early stages of the optimization process and to identify the most promising one at the latter stages of the optimization process. This PSO variant is combined with two local searches which improve the ability of the algorithm in both finding feasible regions and higher quality solutions. This method is further hybridized with covariance matrix adaptation evolutionary strategy (CMA-ES) to enhance its ability to improve the solutions at the latter stages of the optimization process. Results generated by this hybrid method are compared with the results of several other state-of-the-art methods in dealing with standard benchmark constraint optimization problems.


      PubDate: 2014-07-25T22:15:39Z
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: June 2014
      Source:Swarm and Evolutionary Computation, Volume 16




      PubDate: 2014-05-04T14:00:02Z
       
  • An artificial bee colony algorithm for image contrast enhancement
    • Abstract: Publication date: June 2014
      Source:Swarm and Evolutionary Computation, Volume 16
      Author(s): Amer Draa , Amira Bouaziz
      Image Enhancement is a crucial phase in almost every image processing system. It aims at improving both the visual and the informational quality of distorted images. Histogram Equalization (HE) techniques are the most popular approaches for image enhancement for they succeed in enhancing the image and preserving its main characteristics. However, using exhaustive approaches for histogram equalisation is an algorithmically complex task. These HE techniques also fail in offering good enhancement if not so good parameters are chosen. So, new intelligent approaches, using Artificial Intelligence techniques, have been proposed for image enhancement. In this context, this paper proposes a new Artificial Bee Colony (ABC) algorithm for image contrast enhancement. A grey-level mapping technique and a new image quality measure are used. The algorithm has been tested on some test images, and the comparisons of the obtained results with the genetic algorithm have proven its superiority. Moreover, the proposed algorithm has been extended to colour image enhancement and given very promising results. Further qualitative and statistical comparisons of the proposed ABC to the Cuckoo Search (CS) algorithm are also presented in the paper; not only for the adopted grey-level mapping technique, but also with using another common transformation, generally called the local/global transformation.


      PubDate: 2014-05-04T14:00:02Z
       
  • A multi-objective supply chain optimisation using enhanced Bees Algorithm
           with adaptive neighbourhood search and site abandonment strategy
    • Abstract: Publication date: Available online 26 April 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Baris Yuce , Ernesto Mastrocinque , Alfredo Lambiase , Michael S. Packianather , Duc Truong Pham
      In this paper, an enhanced version of the Bees Algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of a given supply chain problem in order to minimise the total cost and the total lead-time. The new Bees Algorithm includes an adaptive neighbourhood size change and site abandonment (ANSSA) strategy which is an enhancement to the basic Bees Algorithm. The supply chain case study utilised in this work is taken from literature and several experiments have been conducted in order to show the performances, the strength, the weaknesses of the proposed method and the results have been compared to those achieved by the basic Bees Algorithm and Ant Colony optimisation. The results show that the proposed ANSSA-based Bees Algorithm is able to achieve better Pareto solutions for the supply chain problem.


      PubDate: 2014-05-04T14:00:02Z
       
  • Comparison of emerging metaheuristic algorithms for optimal hydrothermal
           system operation
    • Abstract: Publication date: Available online 24 April 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Martha P. Camargo , José L. Rueda , István Erlich , Osvaldo Añó
      Optimal hydrothermal system operation (OHSO) is one of the complex and hard-to-solve problems in power system field due to its nonlinear, dynamic, stochastic, non-separable and non-convex nature. Traditionally, this problem has been solved through classical optimization algorithms, which require some approximations to tackle a more tractable variant of the original problem formulation. Metaheuristic optimization has undergone a significant development in recent years, thus, there is a variety of tools with different conceptual differences, which offer a great potential for solving OHSO without extensive simplifications. This paper provides a comparative study on the application of six emerging metaheuristic algorithms to OHSO, namely, the Comprehensive Learning Particle Swarm Optimizer (CLPSO), Genetic algorithm with Multi-Parent Crossover (GA-MPC), Differential Evolution with Adaptive Crossover Operator (DE-ACO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Linearized Biogeography-based Optimization (LBBO), and the Hybrid Median-Variance Mapping Optimization (MVMO-SH). Since these tools have been successfully applied to other hard-to-solve optimization problems, the goal is to ascertain their effectiveness when adapted to tackle the OHSO problem by evaluating their performance in terms of convergence speed, achieved optimum solutions, and computing effort. Numerical experiments, performed on a test system composed by four cascaded hydro plants and an equivalent thermal plant, highlight the relevance of the adopted global search mechanisms, especially for LBBO and MVMO-SH. A nonlinear programming (NLP) algorithm is used as reference to validate the results.


      PubDate: 2014-05-04T14:00:02Z
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: April 2014
      Source:Swarm and Evolutionary Computation, Volume 15




      PubDate: 2014-05-04T14:00:02Z
       
  • Evolution on trees: On the design of an evolution strategy for
           scenario-based multi-period portfolio optimization under transaction costs
           
    • Abstract: Publication date: Available online 28 March 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Hans-Georg Beyer , Steffen Finck , Thomas Breuer
      Scenario-based optimization is a problem class often occurring in finance, planning and control. While the standard approach is usually based on linear stochastic programming, this paper develops an Evolution Strategy (ES) that can be used to treat nonlinear planning problems arising from Value at Risk (VaR)-constraints and not necessarily proportional transaction costs. Due to the VaR-constraints the optimization problem is generally of non-convex type and its decision version is already NP-complete. The developed ES is the first algorithm in the field of evolutionary and swarm intelligence that tackles this kind of optimization problem. The algorithm design is based on the covariance matrix self-adaptation ES (CMSA-ES). The optimization is performed on scenario trees where in each node specific constraints (balance equations) must be fulfilled. In order to evaluate the performance of the ES proposed, instances of increasing problem hardness are considered. The application to the general case with nonlinear node constraints shows not only the potential of the ES designed, but also its limitations. The latter are basically determined by the high dimensionalities of the search spaces defined by the scenario trees.


      PubDate: 2014-05-04T14:00:02Z
       
  • Population statistics for particle swarm optimization: Resampling methods
           in noisy optimization problems
    • Abstract: Publication date: Available online 12 March 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Juan Rada-Vilela , Mark Johnston , Mengjie Zhang
      Particle Swarm Optimization (PSO) is a metaheuristic whose performance deteriorates significantly when utilized on optimization problems subject to noise. On these problems, particles eventually fail to distinguish good from bad solutions because their objective values are corrupted by noise. Specifically, the effect of noise causes particles to suffer from deception when they do not select their true neighborhood best solutions, from blindness when they ignore better solutions, and from disorientation when they prefer worse solutions. Resampling methods reduce the presence of these conditions by re-evaluating the solutions multiple times and better estimating their true objective values with a sample mean over the evaluations. PSO with Equal Resampling (PSO-ER) finds better solutions than the regular PSO thanks mainly to the reduction of deception and blindness, as has been found by utilizing a set of population statistics that track the presence of these conditions throughout the search process. However, the solutions of PSO-ER have been reported to be worse than those of state-of-the-art resampling-based PSO algorithms, and the underlying reasons are not known because the population statistics for such algorithms have never been computed. In this article, we study the population statistics for a new extension to PSO-ER that further reduces the presence of blindness, and for state-of-the-art resampling-based PSO algorithms. Experiments on 20 large-scale benchmark functions subject to different levels of noise show that our new algorithm succeeds at reducing blindness and finding better solutions than PSO-ER. However, the population statistics for state-of-the-art resampling-based PSO algorithms show that their particles suffer even less from deception, blindness and disorientation, and therefore find much better solutions.


      PubDate: 2014-05-04T14:00:02Z
       
  • Directed Bee Colony Optimization Algorithm
    • Abstract: Publication date: Available online 12 March 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Rajesh Kumar
      The paper presents a new optimization algorithm inspired by group decision-making process of honey bees. The honeybees search for the best nest site among many possible sites taking care of both speed and accuracy. The nest site selection is analogous to finding the optimality in an optimization process. Such similarities between two processes have been used to cultivate a new algorithm by learning from each other. Various experiments have been conducted for better understanding of the algorithm. A comprehensive experimental investigation on the choice of various parameters such as number of bees, starting point for exploration, choice of decision process etc. has been made, discussed and used to formulate a more accurate and robust algorithm. The proposed Directed Bee Colony algorithm (DBC) has been tested on various benchmark optimization problems. To investigate the robustness of DBC, the scalability study is also conducted. The experiments conducted clearly show that the DBC generally outperformed the other approaches. The proposed algorithm has exceptional property of generating a unique optimal solution in comparison to earlier nature inspired approaches and therefore, can be a better option for real-time online optimization problems.


      PubDate: 2014-05-04T14:00:02Z
       
  • Context aware filtering using social behavior of frogs
    • Abstract: Publication date: Available online 3 March 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Shikha Mehta , Hema Banati
      The problem of information overload surfaced with the emergent popularity of dynamic web applications. To tackle this issue, various context awareness approaches have been developed to filter the information. Conventional context aware social filtering techniques predominantly focus on time and location as context of the users. However, another relevant context that of user׳s demographic information is often left out. The paper presents demographic context based filtering using social behavior of frogs. The approach employs shuffled frog leaping algorithm (SFLA) to perform the context modeling and handle the sparsity and scalability issues in social filtering. The work proposes two distinct methodologies to model the demographic context – SFLA based Contextual two dimensional (SC2D) and SFLA based Contextual three dimensional (SC3D) approach. SC2D approach primarily develops a model based on social behavior and subsequently incorporates the personal demographic (occupation, gender, etc.) context to compute the most relevant items. In the SC3D approach, personal demographic context is amalgamated with social behavior to develop the model and thereafter a contextual model is used to generate most relevant items. Experimental studies revealed that SC2D approach is able to reduce the error up to 15% and 8% as compared to MAC2D and GAC2D, respectively, and SC3D approach improves the accuracy upto 31% with respect to MAC3D and upto 26% as compared to GAC3D. Analysis of variance (ANOVA) test results for all approaches corroborate that the differences between the means of SC2D, MAC2D and GAC2D and SC3D, MAC3D and GAC3D are highly significant. These results improve confidence in SFLA as a better optimization algorithm for context aware filtering.


      PubDate: 2014-05-04T14:00:02Z
       
  • Soccer league competition algorithm: A novel meta-heuristic algorithm for
           optimal design of water distribution networks
    • Abstract: Publication date: Available online 18 February 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Naser Moosavian , Babak Kasaee Roodsari
      Water distribution networks are one of the most important elements in the urban infrastructure system and require huge investment for construction. Optimal design of water systems is classified as a large combinatorial discrete non-linear optimization problem. The main concern associated with optimization of water distribution networks is related to the nonlinearity of discharge-head loss equation, availability of the discrete nature of pipe sizes, and constraints, such as conservation of mass and energy equations. This paper introduces an efficient technique, entitled Soccer League Competition (SLC) algorithm, which yields optimal solutions for design of water distribution networks. Fundamental theories of the method are inspired from soccer leagues and based on the competitions among teams and players. Like other meta-heuristic methods, the proposed technique starts with an initial population. Population individuals (players) are in two types: fixed players and substitutes that all together form some teams. The competition among teams to take the possession of the top ranked positions in the league table and the internal competitions between players in each team for personal improvements are used for simulation purpose and convergence of the population individuals to the global optimum. Results of applying the proposed algorithm in three benchmark pipe networks show that SLC converges to the global optimum more reliably and rapidly in comparison with other meta-heuristic methods.


      PubDate: 2014-05-04T14:00:02Z
       
  • Research on particle swarm optimization based clustering: A systematic
           review of literature and techniques
    • Abstract: Publication date: Available online 17 February 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Shafiq Alam , Gillian Dobbie , Yun Sing Koh , Patricia Riddle , Saeed Ur Rehman
      Optimization based pattern discovery has emerged as an important field in knowledge discovery and data mining (KDD), and has been used to enhance the efficiency and accuracy of clustering, classification, association rules and outlier detection. Cluster analysis, which identifies groups of similar data items in large datasets, is one of its recent beneficiaries. The increasing complexity and large amounts of data in the datasets have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. Swarm intelligence (SI) is one such optimization technique whose algorithms have successfully been demonstrated as solutions for different data clustering domains. In this paper we investigate the growth of literature in SI and its algorithms, particularly Particle Swarm Optimization (PSO). This paper makes two major contributions. Firstly, it provides a thorough literature overview focusing on some of the most cited techniques that have been used for PSO-based data clustering. Secondly, we analyze the reported results and highlight the performance of different techniques against contemporary clustering techniques. We also provide an brief overview of our PSO-based hierarchical clustering approach (HPSO-clustering) and compare the results with traditional hierarchical agglomerative clustering (HAC), K-means, and PSO clustering.


      PubDate: 2014-05-04T14:00:02Z
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: February 2014
      Source:Swarm and Evolutionary Computation, Volume 14




      PubDate: 2014-05-04T14:00:02Z
       
  • A survey on nature inspired metaheuristic algorithms for partitional
           clustering
    • Abstract: Publication date: Available online 17 January 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Satyasai Jagannath Nanda , Ganapati Panda
      The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.


      PubDate: 2014-01-20T23:20:54Z
       
  • Investigating Aesthetic Measures for Unsupervised Evolutionary Art
    • Abstract: Publication date: Available online 15 January 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Eelco den Heijer , A.E. Eiben
      We present an extensive study into aesthetic measures in unsupervised evolutionary art (EvoArt). In contrast to several mainstream EvoArt approaches we evolve images without human interaction, using one or more aesthetic measures as fitness functions. We perform a series of systematic experiments, comparing 7 different aesthetic measures through subjective criteria (‘style’) as well as by quantitative measures reflecting properties of the evolved images. Next, we investigate the correlation between aesthetic scores by aesthetic measures and calculate how aesthetic measures judge each others images. Furthermore, we run experiments in which two aesthetic measures are acting simultaneously using a Multi-Objective Evolutionary Algorithm. Hereby we gain insights in the joint effects on the resulting images and the compatibility of different aesthetic measures.


      PubDate: 2014-01-16T19:37:07Z
       
  • A Comparative Performance Assessment of a Set of Multiobjective Algorithms
           for Constrained Portfolio Assets Selection
    • Abstract: Publication date: Available online 16 January 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Sudhansu Kumar Mishra , Ganapati Panda , Ritanjali Majhi
      This paper addresses a realistic portfolio assets selection problem as a multiobjective optimization one, considering the budget, floor, ceiling and cardinality as constraints. A novel multiobjective optimization algorithm, namely, the non-dominated sorting multiobjective particle swarm optimization (NS-MOPSO) has been proposed and employed efficiently to solve this important problem. The performance of the proposed algorithm is compared with four multiobjective evolution algorithms (MOEAs), based on non-dominated sorting, and one MOEA algorithm based on decomposition (MOEA/D). The performance results obtained from the study are also compared with those of single objective evolutionary algorithms, such as the genetic algorithm (GA), tabu search (TS), simulated annealing (SA) and particle swarm optimization (PSO). The comparisons of the performance include three error measures, four performance metrics, the Pareto front and computational time. A nonparametric statistical analysis, using the Sign test and Wilcoxon signed rank test is also performed, to demonstrate the superiority of the NS-MOPSO algorithm. On examining the performance metrics, it is observed that the proposed NS-MOPSO approach is capable of identifying good Pareto solutions, maintaining adequate diversity. The proposed algorithm is also applied to different cardinality constraint conditions, for six different market indices, such as the Hang-Seng in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA, Nikkei 225 in Japan, and BSE-500 in India.


      PubDate: 2014-01-16T19:37:07Z
       
  • Modified Teaching–Learning-based Optimization Algorithm for Global
           Numerical Optimization – A Comparative Study
    • Abstract: Publication date: Available online 3 January 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Suresh Chandra Satapathy , Anima Naik
      Teaching–Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique for global optimization over continuous spaces. Few variants of TLBO have been proposed by researchers to improve the performance of the basic TLBO algorithm. In this paper the authors investigate the performance of a new variant of TLBO called modified TLBO (mTLBO) for global function optimization problems. The performance of mTLBO is compared with the state-of-the art forms of Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC) algorithms. Several advanced variants of PSO, DE and ABC are considered for the comparison purpose. The suite of benchmark functions are chosen from the competition and special session on real parameter optimization under IEEE CEC (Congress on Evolutionary Computation) 2005. Statistical hypothesis testing is undertaken to demonstrate the significance of mTLBO over other investigated algorithms. Finally, the paper investigates the data clustering performance of mTLBO over other evolutionary algorithms on a few standard synthetic and artificial datasets. Results of our work reveal that mTLBO performs better than many other algorithms investigated in this work.


      PubDate: 2014-01-04T19:13:40Z
       
  • Extracting easy to understand summary using differential evolution
           algorithm
    • Abstract: Publication date: Available online 21 December 2013
      Source:Swarm and Evolutionary Computation
      Author(s): K. Nandhini , S.R. Balasundaram
      This paper describes an optimization method based on differential evolution algorithm and its novel application to extract easy to understand summary for improving text readability. The idea is to improve the readability of the given text for reading difficulties using assistive summary. In order to extract easy to understand summary from the given text, an improved differential evolution algorithm is proposed. A new chromosome representation that considers ordering and similarity for extracting cohesive summary. Also a modified crossover operator and mutation operator are designed to generate potential offspring. The application of differential evolution algorithm for maximizing the average similarity and informative score in the candidate summary sentences is proposed. We applied the proposed algorithm in a corpus of educational text from ESL text books and in graded text. The results shows that the summary generated using Differential Evolution algorithm performs better in accuracy, readability and lexical cohesion than existing techniques. The task based evaluation done by target audience also favors the significant effect of assistive summary in improving readability.


      PubDate: 2013-12-22T17:16:47Z
       
  • A constraint handling technique for constrained multi-objective genetic
           algorithm
    • Abstract: Publication date: Available online 21 December 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Qiang Long
      A new constraint handling technique for multi-objective genetic algorithm is proposed in this paper. There are two important issues in multi-objective genetic algorithm, closeness of the obtained solutions to the real Pareto frontier and diversity of the obtained solutions. If considering a constrained multi-objective programming problem, one needs to take account of feasibility of solutions. Thus, in this new constraint handling technique, we systematically take closeness, diversity and feasibility as three objectives in a multi-objective subproblem. And solutions in each iteration are sorted by optimal sequence method based on those three objectives. Then, the solutions inherited to the next generation are selected based on its optimal order. Numerical tests show that the solutions obtained by this method are not only feasible, but also close to the real Pareto front and have good diversity.


      PubDate: 2013-12-22T17:16:47Z
       
  • Quantum inspired genetic algorithm and particle swarm optimization using
           chaotic map model based interference for gray level image thresholding
    • Abstract: Publication date: Available online 21 December 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Sandip Dey , Siddhartha Bhattacharyya , Ujjwal Maulik
      In this paper, two meta-heuristics techniques have been employed to introduce two new quantum inspired meta-heuristic methods, namely Quantum Inspired Genetic Algorithm and Quantum Inspired Particle Swarm Optimization for bi-level thresholding. The proposed methods use Otsu's method, Ramesh's method, Li's method, Shanbag's method and also correlation coefficient as evaluation functions to determine optimal threshold values of gray-level images. They exploit the trivial concepts of quantum computing such as qubits and superposition of states. These properties help to exhibit the feature of parallelism that in turn utilizes the time discreteness of quantum mechanical systems. The proposed methods have been compared with their classical counterparts and later with the quantum evolutionary algorithm (QEA) proposed by Han et al. to evaluate the performance among all participating algorithms for three test images. The optimal threshold values with the corresponding fitness value, standard deviation of fitness and finally the computational time of each method for each test images have been reported. The results prove that the proposed methods are time efficient while compared to their conventional counterparts. Another comparative study of the proposed methods with the quantum evolutionary algorithm (QEA) proposed by Han et al. also reveals the weaknesses of the later.


      PubDate: 2013-12-22T17:16:47Z
       
  • A bumble bees mating optimization algorithm for the open vehicle routing
           problem
    • Abstract: Publication date: Available online 20 December 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Yannis Marinakis , Magdalene Marinaki
      Bumble Bees Mating Optimization (BBMO) algorithm is a relatively new swarm intelligence algorithm that simulates the mating behaviour that a swarm of bumble bees performs. In this paper, an improved version of the BBMO algorithm is presented for successfully solving the Open Vehicle Routing Problem. The main contribution of the paper is that the equation which describes the movement of the drones outside the hive has been replaced by a local search procedure. Thus, the algorithm became more suitable for combinatorial optimization problems. The Open Vehicle Routing Problem (OVRP) is a variant of the classic vehicle routing problem. In the OVRP the vehicles do not return to the depot after the service of the customers. Two sets of benchmark instances were used in order to test the proposed algorithm. The obtained results were very satisfactory as in most instances the proposed algorithm found the best known solutions. More specifically, in the fourteen instances proposed by Christofides, the average quality was 0.09% when a hierarchical objective function was used, where, first, the number of vehicles is minimized and, afterwards, the total travel distance is minimized and the average quality was 0.11% when only the travel distance was minimized while for the eight instances proposed by Li et al. when a hierarchical objective function was used the average quality was 0.06%. The algorithm was, also, compared with a number of metaheuristic, evolutionary and nature inspired algorithms from the literature.


      PubDate: 2013-12-22T17:16:47Z
       
  • Optimal Size and Siting of Multiple Distributed Generators in Distribution
           System using Bacterial Foraging Optimization
    • Abstract: Publication date: Available online 16 December 2013
      Source:Swarm and Evolutionary Computation
      Author(s): A. Mohamed Imran , M. Kowsalya
      Optimal location and size of Distributed Generation (DG) in distribution system plays a significant role in minimizing power losses, operational cost and improving voltage stability. This paper presents a new approach to find the optimal location and size of DG with an objective of minimizing network power losses, operational costs and improving voltage stability. Loss sensitivity factor is used to identify the optimal locations for installation of DG units. Bacterial Foraging Optimization Algorithm (BFOA) is used to find the optimal size of DG. BFOA is a swarm intelligence technique which models the individual and group foraging policies of the E. coli bacteria as a distributed optimization process. The technical constraints of voltage and branch current carrying capacity are included in the assessment of the objective function. Proposed method has been tested on IEEE 33-bus and 69-bus radial distribution systems with various load models at different load levels to demonstrate the performance and effectiveness of the technique.


      PubDate: 2013-12-18T15:01:26Z
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: December 2013
      Source:Swarm and Evolutionary Computation, Volume 13




      PubDate: 2013-11-29T21:38:11Z
       
  • A simulated annealing for multi-criteria optimization problem: DBMOSA
    • Abstract: Publication date: Available online 27 November 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Linzhong Liu , Haibo Mu , Juhua Yang , Xiaojing Li , Fang Wu
      This paper investigates a simulated annealing (SA) for multi-criteria optimization problem (MOP) which incorporates the concept of archive in order to provide a set of tradeoff solutions for the problem under consideration. To determine the acceptance probability of a new solution, an evaluation measure of multi-criteria objective function is proposed which takes into account the dominant relation between the new and current solutions, as well as those in the archive. In addition, a mutation operator is proposed in which the constraints of MOP can be partially handled. To efficiently apply the proposed SA into constrained MOP (CMOP), an equivalent relaxation approach is proposed by which the CMOP, say primal problem, can be equivalently transformed into an unconstrained MOP, say relaxation problem. Based on such a constraints handling technique (CHT), it is theoretically proved that the primal problem and the relaxation problem has the same efficient set (ES) and the proposed CHT is then further extended into two existing multi-criteria optimization (MO) evolutionary algorithms (EA) (MOEA), say AMOSA and NSGA-II, respectively. Two performance metrics are proposed to evaluate the performance of the MOEAs. Finally, a comprehensive comparatively studying among AMOSA, NSGA-II and the proposed SA is performed on some popular benchmarks for MOP algorithms to show the effectiveness of the proposed SA and CHT. The comparative study shows that the overall performance of the proposed SA is generally superior to both of the AMOSA and NSGA-II, particularly, for the MOPs with three objectives the performance of the former is far better than the latter, and the proposed CHT can be very conveniently extended into any of the existing EAs for MOP such that they can be applied to solve the CMOPs.


      PubDate: 2013-11-29T21:38:11Z
       
  • An efficient genetic algorithm for multi-objective solid travelling
           salesman problem under fuzziness
    • Abstract: Publication date: Available online 26 November 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Chiranjit Changdar , G.S. Mahapatra , Rajat Kumar Pal
      In this paper, we have presented a multi-objective solid travelling salesman problem (TSP) in fuzzy environment. The attraction of the solid TSP is that a traveller visits all the cities in his tour using multiple conveyance facilities. Here we consider cost and time as two objectives of the solid TSP. The objective of the study is to find a complete tour such that both the total cost and time are minimized. We consider travelling costs and times for one city to another using different conveyance are different and fuzzy in nature. Since cost and time are considered as fuzzy in nature, so the total cost and time for a particular tour are also fuzzy in nature. To find out Pareto-optimal solution of fuzzy objectives, and use fuzzy possibility and necessity measure approach. A multi-objective genetic algorithm with cyclic crossover, two-point mutation, and refining operation is used to solve the TSP problem. In this paper multi-objective genetic algorithm has been modified by introducing the refining operator. Finally, experimental results are given to illustrate the proposed approach; experimental results obtained are also highly encouraging.


      PubDate: 2013-11-29T21:38:11Z
       
  • A fuzzy time series approach based on weights determined by the number of
           recurrences of fuzzy relations
    • Abstract: Publication date: Available online 31 October 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Vedide Rezan Uslu , Eren Bas , Ufuk Yolcu , Erol Egrioglu
      Fuzzy time series approaches, which do not require the strict assumptions of traditional time series approaches, generally consist of three stages. These are called as the fuzzification of crisp time series observations, the identification of fuzzy relationships and the defuzzification. All of these stages play a very important role on the forecasting performance of the model. Although there are many studies contributing to the stages of fuzzification and determining fuzzy relationships, the number of the studies about the defuzzification stage, which is very important at least as much as the others, is limited. None of them considered the number of recurrence of the fuzzy relationships in the stage of defuzzification. However it is very reasonable to take into account since fuzzy relations and their recurrence number are reflected the nature of the time series. Then the information obtained from the fuzzy relationships can be used in the defuzzification stage. In this study, we take into account the recurrence number of the fuzzy relations in the stage of defuzzification. Then this new approach has been applied to the real data sets which are often used in other studies in literature. The results are compared to the ones obtained from other techniques. Thus it is concluded that the results present superior forecasts performance.


      PubDate: 2013-11-01T21:15:09Z
       
  • Fault tolerant scheduling of hard real-time tasks on multiprocessor system
           using a hybrid genetic algorithm
    • Abstract: Publication date: Available online 24 October 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Abhaya Kumar Samal , Rajib Mall , Chittaranjan Tripathy
      Conventional methods for fault-tolerant scheduling of real-time tasks based on traditional heuristic approach offer poor performance and inefficient system utilization. The primary-backup (PB) approach is often used as a fault-tolerant scheduling technique to guarantee RT tasks to meet their deadline despite the presence of fault. We propose a novel scheduling algorithm using optimization approach based on genetic algorithm (GA) hybridized with knowledge from the real-time task scheduling domain for providing fault-tolerance (FT) in multiprocessor environment. Exhaustive simulation reveals that the new GA based primary-backup fault-tolerant scheduling (PBFTS) approach outperforms other fault-tolerant scheduling schemes in terms of system utilization and efficiency.


      PubDate: 2013-10-27T18:34:58Z
       
  • A two-swarm cooperative particle swarms optimization
    • Abstract: Publication date: Available online 15 October 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Shiyuan Sun , Jianwei Li
      Particle swarm optimization (PSO) is a powerful stochastic evolutionary algorithm based on swarm intelligence. However, the canonical PSO is easily and prematurely trapped in the local optima due to loss of its diversity. Although some improved algorithms retain the diversity, their speeds of convergence become slow. Meanwhile, PSO could only find out the global optimum in a small search interval, which greatly limits its applications to many practical engineering problems. In this study, the two-swarm cooperative particle swarm optimization (TCPSO) is presented. TCPSO can not only catch the global optimum in a large search space such as 2×1010, but also obtains a good balance between the swarm diversity and the convergence speed. It uses two particle swarms, the slave swarm and the master swarm with the clear division of their works. The former particles are updated without using the current velocities, the dimension of each particle learns from the same dimension of its neighboring particle instead of the best-so-far position. These features make the particles of the slave swarm concentrate toward the local optimum, thus accelerating the convergence. The latter particles are updated based on the former particles. And the equation in which the velocities of its particles are updated uses a large inertia weight. The feature of the master swarm keeps its diversity invariant. The experiments on TCPSO through 14 test functions showed that it significantly improves the performance of PSO and possesses the best performance among all the examined problems no matter multimodal or unimodal functions.


      PubDate: 2013-10-19T20:04:51Z
       
  • Adaptive Filtering of EEG/ERP through Noise Cancellers using an Improved
           PSO Algorithm
    • Abstract: Publication date: Available online 11 October 2013
      Source:Swarm and Evolutionary Computation
      Author(s): M.K. Ahirwal , A. Kumar , G.K. Singh
      In this paper, Event related potential (ERP) generated due to hand movement is detected through the adaptive noise canceller (ANC) from the electroencephalogram (EEG) signals. ANCs are implemented with Least mean square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS) and evolutionary algorithm like Particle Swarm Optimization (PSO), Bacteria Foraging Optimization (BFO) techniques, Genetic Algorithm (GA) and Artificial Bee Colony (ABC) optimization technique. Performance of this algorithm is evaluated in terms of signal to noise ratio (SNR) in dB, correlation between resultant and template ERP, and mean value. Testing of their noise attenuation capability is done on EEG contaminated with white noise at different SNR levels. A comparative study of the performance of conventional gradient based methods like LMS, NLMS and RLS, and swarm intelligence based PSO, BFO, GA and ABC techniques is made which reveals that PSO technique gives better performance in average cases of noisy environment with minimum computational complexity.


      PubDate: 2013-10-15T00:38:27Z
       
  • Population-based metaheuristics for continuous Boundary-Constrained
           dynamic multi-objective optimisation problems
    • Abstract: Publication date: Available online 27 September 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Mardé Helbig , Andries P. Engelbrecht
      Most real-world optimisation problems are dynamic in nature with more than one objective, where at least two of these objectives are in conflict with one another. This kind of problems is referred to as dynamic multi-objective optimisation problems (DMOOPs). Most research in multi-objective optimisation (MOO) has focussed on static MOO (SMOO) and dynamic single-objective optimisation. However, in recent years, algorithms were proposed to solve dynamic MOO (DMOO). This article provides an overview of the algorithms that were proposed in the literature to solve DMOOPs. In addition, challenges, practical aspects and possible future research directions of DMOO are discussed.


      PubDate: 2013-09-28T17:14:17Z
       
  • Improved binary artificial fish swarm algorithm for the 0–1
           multidimensional knapsack problems
    • Abstract: Publication date: Available online 20 September 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Md. Abul Kalam Azad , Ana Maria A.C. Rocha , Edite M.G.P. Fernandes
      The 0–1 multidimensional knapsack problem (MKP) arises in many fields of optimization and is NP-hard. Several exact as well as heuristic methods exist. Recently, an artificial fish swarm algorithm has been developed in continuous global optimization. The algorithm uses a population of points in space to represent the position of fish in the school. In this paper, a binary version of the artificial fish swarm algorithm is proposed for solving the 0–1 MKP. In the proposed method, a point is represented by a binary string of 0/1 bits. Each bit of a trial point is generated by copying the corresponding bit from the current point or from some other specified point, with equal probability. Occasionally, some randomly chosen bits of a selected point are changed from 0 to 1, or 1 to 0, with an user defined probability. The infeasible solutions are made feasible by a decoding algorithm. A simple heuristic add_item is implemented to each feasible point aiming to improve the quality of that solution. A periodic reinitialization of the population greatly improves the quality of the solutions obtained by the algorithm. The proposed method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method gives a competitive performance when solving this kind of problems.


      PubDate: 2013-09-22T17:00:52Z
       
  • An evolutionary clustering algorithm based on temporal features for
           dynamic Recommender Systems
    • Abstract: Publication date: Available online 29 August 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Chhavi Rana , Sanjay Kumar Jain
      The use of internet and Web services is changing the way we use resources and communicate since the last decade. Although, this usage has made life easier in many respects still the problem of finding relevant information persists. A naïve user faces the problem of information overload and continuous flow of new information makes the problem more complex. Furthermore, user′s interests also keeps on changing with time. Several techniques deal with this problem and data mining is widely used among them. Recommender Systems (RSs) assist users in finding relevant information on the web and are mostly based on data mining algorithms. This paper addresses the problem of user requirements changing over a period of time in seeking information on web and how RSs deal with them. We propose a Dynamic Recommender system (DRS) based on evolutionary clustering algorithm. This clustering algorithm makes clusters of similar users and evolves them depicting accurate and relevant user preferences over time. The proposed approach performs an optimization of conflicting parameters instead of using the traditional evolutionary algorithms like genetic algorithm. The algorithm has been empirically tested and compared with standard recommendation algorithms and it shows considerable improvement in terms of quality of recommendations and computation time.


      PubDate: 2013-09-02T17:09:59Z
       
  • A Novel Technique for Blind Source Separation Using Bees Colony Algorithm
           and Efficient Cost Functions
    • Abstract: Publication date: Available online 17 August 2013
      Source:Swarm and Evolutionary Computation
      Author(s): A. Ebrahimzadeh , S. Mavaddati
      Blind source separation (BSS) technique plays an important role in many areas of signal processing. A BSS technique separates the mixed signals blindly without information about the mixing system. This paper proposes a novel BSS technique using the bees colony algorithm (BCA) in order to achieve the de-mixing system. Cost function is one the important modules for operation of the BCA. So, we have investigated different types of the cost function. These cost functions are based on the balanced combination of two important paradigms, i.e., higher order statistics and information theory. Experimental results show the proposed technique has high separation accuracy, robustness against the local minima, high degree of flexibility and high speed of convergence in noisy and noiseless environments.


      PubDate: 2013-08-21T08:07:44Z
       
  • A Gravitational Search Algorithm for Multimodal Optimization
    • Abstract: Publication date: Available online 20 August 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Sajjad Yazdani , Hossein Nezamabadi-pour , Shima Kamyab
      Gravitational search algorithm (GSA) has been recently presented as a new heuristic search algorithm with good results in real-valued and binary encoded optimization problems which is categorized in swarm intelligence optimization techniques. The aim of this article is to show that GSA is able to find multiple solutions in multimodal problems. Therefore, in this study, a new technique, namely Niche GSA (NGSA) is introduced for multimodal optimization. NGSA extends the idea of partitioning the main population (swarm) of masses into smaller sub-swarms and also preserving them by introducing three strategies: a K -nearest neighbors (K-NN) strategy, an elitism strategy and modification of active gravitational mass formulation. To evaluate the performance of the proposed algorithm several experiments are performed. The results are compared with those of state-of-the-art niching algorithms. The experimental results confirm the efficiency and effectiveness of the NGSA in finding multiple optima on the set of unconstrained and constrained standard benchmark functions.


      PubDate: 2013-08-21T08:07:44Z
       
  • Enhancing Collaborative Filtering Recommendations by Utilizing
           Multi-objective Particle Swarm Optimization Embedded Association Rule
           Mining
    • Abstract: Publication date: Available online 30 July 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Shweta Tyagi , Kamal K. Bharadwaj
      Collaborative Filtering (CF) is the most prevalent technique in recommender systems and facilitates the high-quality recommendations by identifying similar users based on their logged history of prior transactions. However, accuracy of recommendations and sparsity are still major concerns related to CF recommendation techniques. Recent research in CF is investigating the use of Association Rule Mining (ARM) for extracting high-level information and thereby providing more accurate recommendations. However, determination of the threshold values for support and confidence measures affect the quality of association rules. Moreover, the traditional ARM algorithms are based on market basket analysis and therefore degrade computation efficiency by mining too many association rules which are not appropriate for a given user. The proposed approach attempts to improve the quality of recommendations through the application of Multi-objective Particle Swarm Optimization (MOPSO) algorithm for ARM in the framework of CF. Consequently, by considering support and confidence measures as different objectives, the MOPSO based ARM model extracts only useful and eminent direct association rules which are optimal in the wider sense that no other rules are superior to them when both objectives are simultaneously considered. In addition, computational efficiency is enhanced by mining rules only for the given user and over the related transactional database. Further, the present work explores the indirect (transitive) association between users as well as between items for providing more accurate recommendations even with highly sparse history of transactions. In order to evaluate the effectiveness of our approach, we conducted an experimental study using the MovieLens data set. Experimental results clearly reveal that the proposed method consistently outperform other traditional CF based methods as measured by recommendation accuracy, precision, and recall.


      PubDate: 2013-07-30T21:24:09Z
       
  • Stability of Pareto optimal Allocation of Land Reclamation by Multistage
           Decision-based Multipheromone Ant Colony Optimization
    • Abstract: Publication date: Available online 28 June 2013
      Source:Swarm and Evolutionary Computation
      Author(s): A. Mousa , I.M. El-Desoky
      The assignment of multiobjective human resources is a very important phase of the decision-making process, especially with respect to research and development projects where performance strongly depends on human resources capabilities. Unfortunately, the input data or related parameters are frequently imprecise / fuzzy owing to incomplete or unobtainable information, which can be represented as a fuzzy numbers. This paper presents a multiobjective multipheromone ant colony optimization approach (MM-ACO) with an application in fuzzy multiobjective human resource allocation problem. Our approach has two characteristic features. Firstly, a set of nondominated solutions is obtained by exploring the optimal Pareto frontier using different α cut level and subsequently, based on the definition of Pareto stability, the Pareto frontier may be reduced to manageable sizes (i.e., Stable Pareto optimal solutions) where in a practical sense only Pareto optimal solutions that are stable are of interest, since there are always uncertainties associated with the efficiency data. Furthermore, we provided an example of optimum utilization of human resources in reclamation of derelict land in Toshka-Egypt.


      PubDate: 2013-07-01T21:36:46Z
       
  • Metaheuristic algorithms for computing capacitated dominating set with
           uniform and variable capacities
    • Abstract: Publication date: Available online 25 June 2013
      Source:Swarm and Evolutionary Computation
      Author(s): Anupama Potluri , Alok Singh
      The Minimum Capacitated Dominating Set (CAPMDS) problem is the problem of finding a dominating set of minimum cardinality with the additional constraint that the nodes dominated do not exceed the capacity of the respective dominating nodes. Being a generalization of the dominating set problem, CAPMDS is also NP - hard . In this paper, we study the use of Metaheuristic techniques like Genetic Algorithms (GA) and Ant Colony Optimization (ACO) for solving the CAPMDS problem in graphs with uniform and variable capacity for the nodes. To our knowledge, this is the first attempt at applying the metaheuristic techniques to this problem. We show that the standard GA needs to be seeded with solutions using a heuristic we designed, for the GA to perform well. Similarly, we show that using a pre-processing step for the ACO algorithm improves its performance. When the capacity of the nodes is small, the metaheuristics return a much better solution than the heuristic. However, as the capacity increases or average degree of the graph increases, the solution returned by them does not improve significantly.


      PubDate: 2013-06-27T21:37:20Z
       
 
 
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