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Journal Cover   Swarm and Evolutionary Computation
  [SJR: 5.631]   [H-I: 13]   Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2210-6502
   Published by Elsevier Homepage  [2588 journals]
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: April 2015
      Source:Swarm and Evolutionary Computation, Volume 21

      PubDate: 2015-03-19T17:14:49Z
  • Prediction of porosity and thermal diffusivity in a porous fin using
           differential evolution algorithm
    • Abstract: Publication date: Available online 19 March 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Ranjan Das , Dilip K. Prasad
      In this paper, simultaneous inverse prediction of two parameters such as the porosity and thermal diffusivity of the fluid in a porous fin is done for satisfying a given temperature distribution. Only three temperature measurements are assumed to be available on the surface of the fin and prediction of the parameters is accomplished by using the differential evolution (DE)-based optimization technique. It is shown that the present problem is inherently ill-posed in terms of the retrieval of the value of fluid thermal diffusivity for which many possible solutions exist, which is expected to adapt the fin under different conditions. In the present work, two numerical examples provide engineering insight into the problem of designing porous fins using good thermal conductors like aluminum and copper alongwith the working of DE. Finally, the efficacy of DE for the present problem is also shown by comparing its performance with few other optimization methods.

      PubDate: 2015-03-19T17:14:49Z
  • An evolutionary based topological optimization strategy for consensus
           based clock synchronization protocols in wireless sensor network
    • Abstract: Publication date: Available online 11 March 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Niranjan Panigrahi , Pabitra Mohan Khilar
      Recently, Consensus based Clock Synchronization (CCS) algorithms have gained much attention in wireless sensor networks due to its simplicity, distributed nature and robustness. But, most of the algorithms are “all node based”, i.e., every node iterates the consensus algorithm to reach the synchronized state. This increases the overall message complexity, imposes congestion and delay in the network and high consumption of energy. In an energy constraint environment, it is desirable that a subset of sensors along with a limited number of neighboring sensors should be selected a priori such that the message complexity will be minimized and energy can be saved. Further, the selection of “subset” sensors must ensure connectivity for consensus propagation to achieve network wide synchronization and the neighboring sensors must be assigned in such a way that the delay must be minimized and balanced for faster consensus convergence. The overall problem is formulated as a Connected Dominating Set based Delay Balanced Topology (CDSDBT) problem and is shown to be NP-complete. To make the problem tractable, a Random Weighted Genetic Algorithm (RWGA) based strategy is proposed to handle the trade-off between the objective functions and to select the pareto optimal solution (topology). Simulation results show that using the proposed strategy, the performance of some state-of-the-art CCS protocols have been improved significantly over their “all node based” counterpart. A comparative analysis is also carried out with recent and state-of-the-art GA based Minimum Connected Dominating Set (GAMCDS) strategy and GA based Load Balanced Connected Dominating Set (GALBCDS) strategy for the test CCS protocols which are used as topological backbone for other protocols and applications.

      PubDate: 2015-03-14T16:58:42Z
  • Anatomy of the fitness landscape for dense graph-colouring problem
    • Abstract: Publication date: Available online 9 March 2015
      Source:Swarm and Evolutionary Computation
      Author(s): M.-H. Tayarani-N. , Adam Prügel-Bennett
      Graph-colouring is one of the best-known combinatorial optimisation problems. This paper provides a systematic analysis of many properties of the fitness landscape for random instances as a function of both the problem size and the number of colours used. The properties studied include both statistical properties of the bulk of the states, such as the distribution of fitnesses and the auto-correlation, but also properties related to the local optima of the problem. These properties include the mean time to reach the local optima, the number of local optima and the probability of reaching local optima of a given cost, the average distance between global optima and between local optima of a given cost and the closest local optimum, the expected cost as a function of the distance from a configuration and the fitness–distance correlation. Finally, an analysis of how a successful algorithm exploits the fitness distance correlation is carried out.

      PubDate: 2015-03-14T16:58:42Z
  • Improved sampling using loopy belief propagation for probabilistic model
           building genetic programming
    • Abstract: Publication date: Available online 6 March 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Hiroyuki Sato , Yoshihiko Hasegawa , Danushka Bollegala , Hitoshi Iba
      In recent years, probabilistic model building genetic programming (PMBGP) for program optimization has attracted considerable interest. PMBGPs generally use probabilistic logic sampling (PLS) to generate new individuals. However, the generation of the most probable solutions (MPSs), i.e., solutions with the highest probability, is not guaranteed. In the present paper, we introduce loopy belief propagation (LBP) for PMBGPs to generate MPSs during the sampling process. We selected program optimization with linkage estimation (POLE) as the foundation of our approach and we refer to our proposed method as POLE-BP. We apply POLE-BP and existing methods to three benchmark problems to investigate the effectiveness of LBP in the context of PMBGPs, and we describe detailed examinations of the behaviors of LBP. We find that POLE-BP shows better search performance with some problems because LBP boosts the generation of building blocks.

      PubDate: 2015-03-09T16:09:53Z
  • Performance analysis of the multi-objective ant colony optimization
           algorithms for the traveling salesman problem
    • Abstract: Publication date: Available online 3 March 2015
      Source:Swarm and Evolutionary Computation
      Author(s): I.D.I.D. Ariyasingha , T.G.I. Fernando
      Most real world combinatorial optimization problems are difficult to solve with multiple objectives which have to be optimized simultaneously. Over the last few years, researches have been proposed several ant colony optimization algorithms to solve multiple objectives. The aim of this paper is to review the recently proposed multi-objective ant colony optimization (MOACO) algorithms and compare their performances on two, three and four objectives with different numbers of ants and numbers of iterations. Moreover, a detailed analysis is performed for these MOACO algorithms by applying them on several multi-objective benchmark instances of the traveling salesman problem. The results of the analysis have shown that most of the considered MOACO algorithms obtained better performances for more than two objectives and their performance depends slightly on the number of objectives, number of iterations and number of ants used.

      PubDate: 2015-03-04T15:33:29Z
  • Population statistics for particle swarm optimization: Hybrid methods in
           noisy optimization problems
    • Abstract: Publication date: Available online 17 February 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Juan Rada-Vilela , Mark Johnston , Mengjie Zhang
      Particle swarm optimization (PSO) is a metaheuristic designed to find good solutions to optimization problems. However, when optimization problems are subject to noise, the quality of the resulting solutions significantly deteriorates, hence prompting the need to incorporate noise mitigation mechanisms into PSO. Based on the allocation of function evaluations, two opposite approaches are generally taken. On the one hand, resampling-based PSO algorithms incorporate resampling methods to better estimate the objective function values of the solutions at the cost of performing fewer iterations. On the other hand, single-evaluation PSO algorithms perform more iterations at the cost of dealing with very inaccurately estimated objective function values. In this paper, we propose a new approach in which hybrid PSO algorithms incorporate noise mitigation mechanisms from the other two approaches, and the quality of their results is better than that of the state of the art with a few exceptions. The performance of the algorithms is analyzed by means of a set of population statistics that measure different characteristics of the swarms throughout the search process. Amongst the hybrid PSO algorithms, we find a promising algorithm whose simplicity, flexibility and quality of results questions the importance of incorporating complex resampling methods into state-of-the-art PSO algorithms.

      PubDate: 2015-02-23T14:11:48Z
  • Novel search scheme for multi-objective evolutionary algorithms to obtain
           well-approximated and widely spread Pareto solutions
    • Abstract: Publication date: Available online 10 February 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Satoru Hiwa , Masashi Nishioka , Tomoyuki Hiroyasu , Mitsunori Miki
      In multi-objective optimization, the quality of Pareto-optimal solutions is evaluated by the efficiency of the optimal front (proximity), uniformity, and spread. This paper introduces a novel search scheme for multi-objective evolutionary algorithms (MOEAs), whose solutions demonstrate improved proximity and spread metrics. Our proposed scheme comprises two search phases with different search objectives. The first phase uses a reference-point-based approach to improve proximity; the second phase adopts a distributed-cooperation scheme (DC-scheme) to broaden the range of solutions. We experimentally investigate the effectiveness of our proposed scheme on the walking fish group (WFG) test suite of scalable multi-objective problems. Finally, we show the applicability of the proposed scheme to various types of MOEAs.

      PubDate: 2015-02-15T12:06:24Z
  • A genetic algorithm for unconstrained multi-objective optimization
    • Abstract: Publication date: Available online 27 January 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Qiang Long , Changzhi Wu , Tingwen Huang , Xiangyu Wang
      In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-objective genetic algorithm (MOGA) is a direct method for multi-objective optimization problems. Compared to the traditional multi-objective optimization method whose aim is to find a single Pareto solution, MOGA tends to find a representation of the whole Pareto frontier. During the process of solving multi-objective optimization problems using genetic algorithm, one needs to synthetically consider the fitness, diversity and elitism of solutions. In this paper, more specifically, the optimal sequence method is altered to evaluate the fitness; cell-based density and Pareto-based ranking are combined to achieve diversity; and the elitism of solutions is maintained by greedy selection. To compare the proposed method with others, a numerical performance evaluation system is developed. We test the proposed method by some well known multi-objective benchmarks and compare its results with other MOGASs׳; the result show that the proposed method is robust and efficient.

      PubDate: 2015-02-02T13:19:39Z
  • A Molecular Simulation Based Computational Intelligence Study of a
    • Abstract: Publication date: Available online 23 January 2015
      Source:Swarm and Evolutionary Computation
      Author(s): A. Garg , V. Vijayaraghavan , Jasmine Siu Lee Lam , Pravin M Singru , Liang Gao
      Determining the optimum input parameter settings (temperature, rotational velocity and feed rate) in optimizing the properties (strength and time) of the nano-drilling process can result in an improvement in its environmental performance. This is because the rotational velocity is an essential component of power consumption during drilling and therefore by determining its appropriate value required in optimization of properties, the trial-and-error approach that normally results in loss of power and waste of resources can be avoided. However, an effective optimization of properties requires the formulation of the generalized and an explicit mathematical model. In the present work, the nano-drilling process of Boron Nitride Nanosheet (BNN) panels is studied using an explicit model formulated by a molecular dynamics (MD) based computational intelligence (CI) approach. The approach consists of nano scale modeling using MD simulation which is further fed into the paradigm of a CI cluster comprising genetic programming, which was specifically designed to formulate the explicit relationship of nano-machining properties of BNN panel with respect to process temperature, feed and rotational velocity of drill bit. Performance of the proposed model is evaluated against the actual results. We find that our proposed integrated CI model is able to model the nano-drilling process of BNN panel very well, which can be used to complement the analytical solution developed by MD simulation. Additionally, we also conducted sensitivity and parametric analysis and found that the temperature has the least influence, whereas the velocity has the highest influence on the properties of nano-drilling process of BNN panel.

      PubDate: 2015-01-27T12:43:44Z
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: February 2015
      Source:Swarm and Evolutionary Computation, Volume 20

      PubDate: 2015-01-22T12:06:34Z
  • The dynamic vehicle routing problem: Solution with hybrid metaheuristic
    • Abstract: Publication date: Available online 6 January 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Jalel Euchi , Adnan Yassine , Habib Chabchoub
      The increased awareness in just-in-time supply systems with the apparition of the new advances in communication and information technologies, have recently led researchers to focus on dynamic vehicle routing problem (DVRP). In DVRP, client requests are not known and are exposed after making some decisions. An alternative variant dynamic pickup and delivery strategy of the vehicle routing problem arises when new customers appear in the tours after the starting visit. The DVRP are among the more important and more challenging extensions of VRP. The purpose of this paper is to propose an Artificial Ant Colony based on 2_Opt local search (AAC_2_Opt) to solve the DVRP. We demonstrate the effectiveness of our approach by comparing its results with those of existing methods in the literature on the same tests problems. The AAC_2_Opt algorithm can efficiently optimize the routing problem and provide highly competitive solution.

      PubDate: 2015-01-10T11:21:59Z
  • Artificial bees for multilevel thresholding of iris images
    • Abstract: Publication date: Available online 23 December 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Amira Bouaziz , Amer Draa , Salim Chikhi
      In this paper, a multilevel thresholding based on Artificial Bee Colony metaheuristic is proposed as a pre-segmentation step in the iris detection process. Multilevel thresholding helps in the unification of the iris region and the attenuation of the noise outside and inside the iris region that mainly affects the process of iris segmentation. Since it depends on exhaustive search, multilevel thresholding is time consuming especially if the number of thresholds is not restricted, though it yields convenient results. Two variants of Artificial Bee Colony (ABC) metaheuristic, namely, the basic ABC and the G-best guided ABC in addition to Cuckoo Search (CS) and Particle Swarm Optimisation (PSO) metaheuristics are then used to look for the best thresholds distribution delimiting the components of the iris image for improving the iris detection results. To test our approach, we have opted for the Integro-differential Operator of Daughman and the Masek method for the principal segmentation process on both the standard databases CASIA and UBIRIS. As a result, qualitatively the segmented iris images are enhanced; numerically the iris detection rate improved and became more accurate.

      PubDate: 2014-12-28T10:41:10Z
  • Soccer league competition algorithm for solving knapsack problems
    • Abstract: Publication date: Available online 23 October 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Naser Moosavian
      Soccer league competition (SLC) algorithm, is a new meta-heuristic optimization technique and has been successfully used to tackle the optimization problems in discrete or continuous space. Fundamental ideas of SLC are inspired by a professional soccer leagues and based on the competitions among teams and players. Population individuals or 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. In this study, an enhanced SLC algorithm is proposed to solve knapsack problems effectively. This new version is free and independent from adjusting the parameters. The experimental results on the some benchmark knapsack problems demonstrate that the proposed SLC is efficient and effective, which outperforms the other algorithms, in terms of the search accuracy, reliability and convergence speed.

      PubDate: 2014-12-22T21:44:55Z
  • Swarm algorithm with adaptive mutation for airfoil aerodynamic design
    • Abstract: Publication date: Available online 23 October 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Manas Khurana , Kevin Massey
      The Particle Swarm Optimization (PSO) method is sensitive to convergence at a sub-optimum solution for complex aerospace design problems. An Adaptive Mutation-Particle Swarm Optimization (AM-PSO) method is developed to address this challenge. A Gaussian-based operator is implemented to induce particle search diversity with probability through mutation. The extent of mutation during the optimization phase is governed by the collective search patterns of the swarm. Accordingly the proposed approach is shown to mitigate convergence at a sub-optimum design while concurrently limiting the computational resources required during the optimization cycle. The swarm algorithm developed is successfully validated on benchmark test functions with results favorably compared against several off-the-shelf methods. The AM-PSO is then used for airfoil re-design at flight envelopes encompassing low-to-high Mach numbers. The drag performances of the optimum airfoils are lower than the baseline shapes with the design effort requiring minimal computational resources relative to the optimization method documented in the literature.

      PubDate: 2014-12-22T21:44:55Z
  • Adapting ant colony optimization to generate test data for software
           structural testing
    • Abstract: Publication date: Available online 28 October 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Chengying Mao , Lichuan Xiao , Xinxin Yu , Jinfu Chen
      In general, software testing has been viewed as an effective way to improve software quality and reliability. However, the quality of test data has a significant impact on the fault-revealing ability of software testing activity. Recently, search-based test data generation has been treated as an operational approach to settle this difficulty. In the paper, the basic ACO algorithm is reformed into discrete version so as to generate test data for structural testing. First, the technical roadmap of combining the adapted ACO algorithm and test process together is introduced. In order to improve algorithm׳s searching ability and generate more diverse test inputs, some strategies such as local transfer, global transfer and pheromone update are defined and applied. The coverage for program elements is a special optimization objective, so the customized fitness function is constructed in our approach through comprehensively considering the nesting level and predicate type of branch. To validate the effectiveness of our ACO-based test data generation method, eight well-known programs are utilized to perform the comparative analysis. The experimental results show that our approach outperforms the existing simulated annealing and genetic algorithm in the quality of test data and stability, and is comparable to particle swarm optimization-based method. In addition, the sensitivity analysis on algorithm parameters is also employed to recommend the reasonable parameter settings for practical applications.

      PubDate: 2014-12-22T21:44:55Z
  • Automatically configuring ACO using multilevel ParamILS to solve
           transportation planning problems with underlying weighted networks
    • Abstract: Publication date: Available online 11 November 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Pengpeng Lin , Jun Zhang , Marco A. Contreras
      Configuring parameter settings for ant colony optimisation (ACO) based algorithms is a challenging and time consuming task, because it usually requires evaluating a large number of parameter combinations to find the most appropriate setting. In this study, a multilevel ParamILS (MParamILS) technique, that combines a graph coarsening method and the ParamILS framework, has been developed for configuring ACO algorithms to solve transportation planning problems with underlying weighted networks. The essential idea is to first use the graph coarsening method to recursively produce a set of increasingly coarser level problems from the original problem, and then apply ParamILS sequentially to the coarser level problems to select high-quality settings from a parameter combination domain. From the coarsest level to the finest (original) level problem, the parameter domain is refined by removing the low-quality settings identified by ParamILS. The size of the combination domain continues to decrease, resulting in fewer number of parameter combinations evaluated at finer level problems, hence the computing time is reduced. The performance of MParamILS was compared with ParamILS. Experimental results showed that MParamILS matches ParamILS in solution quality with significant reduction in computing time for all test cases.

      PubDate: 2014-12-22T21:44:55Z
  • Novel performance metrics for robust multi-objective optimization
    • Abstract: Publication date: Available online 12 November 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Seyedali Mirjalili , Andrew Lewis
      Performance metrics are essential for quantifying the performance of optimization algorithms in the field of evolutionary multi-objective optimization. Such metrics allow researchers to compare different algorithms quantitatively. In the field of robust multi-objective optimization, however, there is currently no performance metric despite its significant importance. This motivates our proposal of three novel specific metrics for measuring the convergence, coverage, and success rate of robust Pareto optimal solutions obtained by robust multi-objective algorithms. The proposed metrics are employed to quantitatively evaluate and compare Robust Multi-objective Particle Swarm Optimization (RMOPSO) and Robust Non-dominated Sorting Genetic Algorithm (RNSGA-II) on seven selected benchmark problems. The results show that the proposed metrics are effective in quantifying the performance of robust multi-objective algorithms in terms of convergence, coverage, and the ratio of the robust/non-robust Pareto optimal solutions obtained.

      PubDate: 2014-12-22T21:44:55Z
  • Artificial bee colony algorithm to design two-channel quadrature mirror
           filter banks
    • Abstract: Publication date: Available online 13 December 2014
      Source:Swarm and Evolutionary Computation
      Author(s): S.K. Agrawal , O.P. Sahu
      Artificial bee colony (ABC) algorithm has been introduced recently for solving optimization problems. The ABC algorithm is based on intelligent foraging behavior of honeybee swarms and has many advantages over earlier swarm intelligence algorithms. In this work, a new method based on ABC algorithm for designing two-channel quadrature mirror filter (QMF) banks with linear phase is presented. To satisfy the perfect reconstruction condition, low-pass prototype filter coefficients are optimized to minimize an objective function. The objective function is formulated as a weighted sum of four terms, pass-band error, and stop-band residual energy of low-pass analysis filter, square error of the overall transfer function at the quadrature frequency and amplitude distortion of the QMF bank. The design results of the proposed method are compared with earlier reported results of particle swarm optimization (PSO), differential-evolution (DE) and conventional optimization algorithms.

      PubDate: 2014-12-22T21:44:55Z
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: December 2014
      Source:Swarm and Evolutionary Computation, Volume 19

      PubDate: 2014-12-22T21:44:55Z
  • A novel framework for retiming using evolutionary computation for high
           level synthesis of digital filters
    • Abstract: Publication date: Available online 12 November 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Deepa Yagain , A. Vijayakrishna
      In this paper, design of a new algorithm and a framework for retiming the DSP blocks based on evolutionary computation process is explained. Optimal DSP blocks such as digital filter design is a high level synthesis problem which includes optimally mapping digital filter specifications on to FPGA (Field Programmable Gate Array) architecture. Retiming is the considered optimization method in this paper which gives optimality in terms of algorithm processing speed and digital filter operating frequency with register count as a constraint. The designed novel algorithm is for the synthesis of high speed digital filters for different signal processing applications based on nature inspired evolutionary computation method. The classical retiming algorithms such as clock period minimization and register minimization that are addressed in the literature provide a single heuristic solution based on the chosen optimization parameter such as clock period. However, for retiming which is multi-objective optimization, evolutionary approach can lead to better results. Using the designed evolutionary computation based retiming method, retimed solution database is generated with higher frequency and different output register counts by searching the digital block solution space. Depending on the clock period and register count constraint, designer can take a design decision. Here, various signal processing designs are used to facilitate the design analysis. Results also show that the CPU processing time needed to compute multiple solutions using the designed algorithm for filter circuits is reduced for designs whose maximum feasible solutions are less than 50. If the circuit is very big with the possible solution space greater than 50 solutions, then algorithm performs slower. A comparison is also provided in the Simulations section with respect to all the existing classical retiming methods in the literature such as clock period and register minimization retiming to prove the concept. Multi-objective genetic algorithms are the considered evolutionary computation method in this paper.

      PubDate: 2014-12-22T21:44:55Z
  • A novel two-level particle swarm optimization approach to train the
           transformational grammar based hidden Markov models for performing
           structural alignment of pseudoknotted RNA
    • Abstract: Publication date: Available online 3 December 2014
      Source:Swarm and Evolutionary Computation
      Author(s): Soniya Lalwani , Rajesh Kumar , Nilama Gupta
      A two-level particle swarm optimization (TL-PSO) algorithm is proposed for training stochastic context-sensitive hidden Markov model (cs-HMM), that addresses a thrust area of bioinformatics i.e. structural alignment of pseudoknotted non-coding RNAs (ncRNAs). Due to the well-conserved sequences and corresponding secondary structures of ncRNAs, the structural information becomes imperative for performing their alignments. Proposed approach is unique in the sense: it is the first idea so far which works on optimization of the model length; also it is the first swarm intelligence technique that is proposed for training csHMM. The two-level strategy with training and cross training sets helps in increasing the diversity of the particles so as to avoid trapping in local optima, yields more accurate estimation parameters, preserves the structure of the model and provides the best compression from the model. TL-PSO yields a trained stochastic model with position-dependent probabilities that achieves high prediction ratios than the compared non-stochastic scoring matrix based csHMM approaches. TL-PSO is also tested solely for sequence alignment of proteins, by training the conventional HMMs. TLPSO-HMM produces an effective framework for sequence alignment in terms of alignment quality and prediction accuracy than the competitive state-of-the-art and family of PSO based algorithms. Conjointly, TLPSO-csHMM finds higher prediction measures than competitive RNA structural alignment techniques for pseudoknotted and non-pseudoknotted RNA structures of diverse complexities.

      PubDate: 2014-12-22T21:44:55Z
  • Selective voltage harmonic elimination in PWM inverter using bacterial
           foraging algorithm
    • Abstract: Publication date: Available online 6 December 2014
      Source:Swarm and Evolutionary Computation
      Author(s): T. Sudhakar Babu , D. Maheswaran , N. Rajasekar
      Pulse width modulation (PWM) techniques are increasingly employed in power electronic circuits. Among the various PWM methods used, selective harmonic elimination PWM (SHEPWM) method is popular and it is widely accepted for its better harmonic elimination capability and its ability to maintain output voltage regulation. However, in this method difficulty persists in the identification of switching instants as it involves non-linear transcendental equations for obtaining the desired solution. In this paper, bacterial foraging algorithm (BFA) method is proposed for switching angle selection in PWM inverter. The problem of voltage harmonic elimination together with output voltage regulation is drafted as an optimization task and the solution is sought through the proposed method. Extensive simulations are carried out using MATLAB/SIMULINK environment under various operating points with different switching pulses per half cycle. To demonstrate the superiority of the proposed method, BFA results are compared with other existing techniques such as Genetic Algorithm and Particle Swarm Optimization method. Further, the obtained simulation results are validated through experimental findings.

      PubDate: 2014-12-22T21:44:55Z
  • 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
    • 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
    • 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
  • 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
  • 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
  • A survey on nature inspired metaheuristic algorithms for partitional
    • 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
  • 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
    • 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
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