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Journal Cover   Swarm and Evolutionary Computation
  [SJR: 5.631]   [H-I: 13]   [0 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2210-6502
   Published by Elsevier Homepage  [2800 journals]
  • Discrete Honeybee Mating Optimization Algorithm for the Routing of
           Battery-operated Automated Guidance Electric Vehicles in Personal Rapid
           Transit Systems
    • Abstract: Publication date: Available online 20 August 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Ezzeddine Fatnassi, Olfa Chebbi, Jouhaina Chaouachi
      Reducing the amount of energy consumed by mass transit systems can be a challenging task. The present study focuses on minimizing the energy consumed by a relatively new transportation system called a personal rapid transit (PRT) system. PRT systems provide automated direct nonstop transit services to their users. This study explores the routing problem associated with PRT where the aim is to minimize the energy consumption while considering the battery capacity of PRT vehicles. The honeybee mating optimization algorithm is adapted to this problem. A specific enhancement procedure is proposed that boosts the performance of the algorithm based on a specific initialization of the population. A multiple-descendant honeybee mating optimization algorithm is also proposed. Finally, the algorithms were verified using a set of 1320 randomly generated instances and extensive statistical analyses were performed to validate the results obtained.

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

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

      PubDate: 2015-08-14T05:56:36Z
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: October 2015
      Source:Swarm and Evolutionary Computation, Volume 24

      PubDate: 2015-08-14T05:56:36Z
  • Unconventional modelling of complex system via cellular automata and
           differential evolution
    • Abstract: Publication date: Available online 14 August 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Martin Kotyrba, Eva Volna, Petr Bujok
      The article deals with principles and utilization possibilities of cellular automata and differential evolution within task resolution and simulation of an epidemic process. The modelling of the spread of epidemics is one of the most widespread and commonly used areas of a modelling of complex systems. The origins of such complexity can be investigated through mathematical models termed ‘cellular automata’. Cellular automata consist of many identical components, each simple, but together capable of complex behaviour. They are analysed both as discrete dynamical systems, and as information-processing systems. Cellular Automata (CA) are well known computational substrates for studying emergent collective behaviour, complexity, randomness and interaction between order and chaotic systems. For the purpose of the article, cellular automata and differential evolution are recognized as an intuitive modelling paradigm for complex systems. The proposed cellular automata supports to find rules of the transition function that represents the model of a studied epidemic. Search for models a studied epidemic belongs to inverse problems whose solution lies in a finding of local rules guaranteeing a desired global behaviour. The epidemic models have the control parameters and their setting significantly influences the behaviour of the models. One way how to get proper values of the control parameters is use evolutionary algorithms, especially differential evolution (DE). Simulations of illness lasting from one to ten days were performed using both described approaches. The aim of the paper is to show a course of simulations for different rules of the transition function and how to find a suitable model of a studied epidemic in the case of inverse problems using a sufficient amount of local rules of a transition function.

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

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

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

      PubDate: 2015-07-28T20:50:49Z
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: August 2015
      Source:Swarm and Evolutionary Computation, Volume 23

      PubDate: 2015-07-13T18:22:03Z
  • A survey on evolutionary algorithms dynamics and its complexity - mutual
           relations, past, present and future
    • Abstract: Publication date: Available online 6 July 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Ivan Zelinka
      Swarm and evolutionary based algorithms represent a class of search methods that can be used for solving optimization problems. They mimic natural principles of evolution and swarm based societies like ants, bees, by employing a population-based approach in mutual communication and information sharing and processing, including randomness. In this paper, history of swarm and evolutionary algorithms are discussed in general as well as their dynamics, structure and behavior. The core of this paper is an overview of an alternative way how dynamics of arbitrary swarm and evolutionary algorithms can be visualized, analyzed and controlled. Also selected representative applications are discussed at the end. Both subtopics are based on interdisciplinary intersection of two interesting research areas: swarm and evolutionary algorithms and complex dynamics of nonlinear systems that usually exhibit very complex behavior.

      PubDate: 2015-07-07T17:01:57Z
  • Solving the Multi-objective Vehicle Routing Problem with Soft Time Windows
           with the Help of Bees
    • Abstract: Publication date: Available online 23 June 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Sumaiya Iqbal , M. Kaykobad , M. Sohel Rahman
      This paper presents a new model and solution for the multi-objective Vehicle Routing Problem with Soft Time Windows (VRPSTW) using a hybrid metaheuristic technique. The proposed methodology is developed on the basics of a new swarm based Artificial Bee Colony (ABC) algorithm combined with two-step constrained local search for neighborhood selection. VRPSTW involves computing the routes of a set of vehicles with fixed capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. Here, the time window constraints are relaxed into “soft”, that is penalty terms are added to the solution cost whenever a vehicle serves a customer outside of his time window. The solution of routing problems with soft time windows has valuable practical applications. This paper uses a direct interpretation of the VRPSTW as a multi-objective optimization problem where the total traveling distance, number of window violations and number of required vehicles are minimized while capacity and time window constraints are met. Our work aims at using ABC inspired foraging behavior of honey bees which balances exploration and exploitation to avoid local optima and reach the global optima. The algorithm is applied to solve the well known benchmark Solomon's problem instances. Experimental results show that our suggested approach is quite effective, as it provides solutions that are competitive with the best known results in the literature. Finally, we present an analysis of our proposed algorithm in terms of computational time.

      PubDate: 2015-06-27T14:31:57Z
  • Comprehensive learning particle swarm optimization with heterogeneous
           population topologies for enhanced exploration and exploitation
    • Abstract: Publication date: Available online 23 June 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Nandar Lynn , Ponnuthurai Nagaratnam Suganthan
      This paper presents a comprehensive learning particle swarm optimization algorithm with enhanced exploration and exploitation, named as “grouped comprehensive learning particle swarm optimization” (GCLPSO). In this algorithm, the swarm population is divided into two subpopulations. Each subpopulation is assigned to focus solely on either exploration or exploitation. Comprehensive learning (CL) strategy is used to generate the exemplars for both subpopulations. In the exploration-subpopulation, the exemplars are generated by using personal best experiences of the particles in the exploration-subpopulation itself. In the exploitation-subpopulation, the personal best experiences of the entire swarm population are used to generate the exemplars. As the exploration-subpopulation does not learn from any particles in the exploitation-subpopulation, the diversity in the exploration-subpopulation can be retained even if the exploitation-subpopulation converges prematurely. The grouped comprehensive learning particle swarm optimization algorithm is tested on shifted and rotated benchmark problems and compared with other recent particle swarm optimization algorithms to demonstrate superior performance of the proposed algorithm over other particle swarm optimization variants.

      PubDate: 2015-06-23T14:15:22Z
  • An Improved Cuckoo Search based Extreme Learning Machine for Medical Data
    • Abstract: Publication date: Available online 10 June 2015
      Source:Swarm and Evolutionary Computation
      Author(s): P. Mohapatra , S. Chakravarty , P.K. Dash
      Machine learning techniques are being increasingly used for detection and diagnosis of diseases for its accuracy and efficiency in pattern classification. In this paper, improved cuckoo search based extreme learning machine (ICSELM) is proposed to classify binary medical datasets. Extreme learning machine (ELM) is widely used as a learning algorithm for training single layer feed forward neural networks (SLFN) in the field of classification. However, to make the model more stable, an evolutionary algorithm, improved cuckoo search (ICS) is used to pre-train ELM by selecting the input weights and hidden biases. Like ELM, Moore–Penrose (MP) generalized inverse is used in ICSELM to analytically determines the output weights. To evaluate the effectiveness of the proposed model, four benchmark datasets, i.e. Breast Cancer, Diabetes, Bupa and Hepatitis from the UCI Repository of Machine Learning are used. A number of useful performance evaluation measures including accuracy, sensitivity, specificity, confusion matrix, Gmean, F-score and norm of the output weights as well as the area under the receiver operating characteristic (ROC) curve are computed. The results are analyzed and compared with both ELM based models like ELM, on-line sequential extreme learning algorithm (OSELM), CSELM and other artificial neural networks i.e multi-layered perceptron (MLP), MLPCS, MLPICS and radial basis function neural network (RBFNN), RBFNNCS, RBFNNICS. The experimental results demonstrate that the ICSELM model outperforms other models.

      PubDate: 2015-06-13T12:39:45Z
  • An efficient biogeography based optimization algorithm for solving
           reliability optimization problems
    • Abstract: Publication date: Available online 30 May 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Harish Garg
      The objective of this paper is to solve the reliability redundancy allocation problems of series-parallel system under the various nonlinear resource constraints using the penalty guided based biogeography based optimization. In this type of problem both the number of redundant components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximize the reliability of the system. A parameter-free penalty function has been taken which encourages the algorithm to explore within the feasible region and the near feasible region, and discourage the infeasible solutions. Four benchmark problems with the reliability, redundancy allocation problems are taken to demonstrate the approach and it has been shown by comparison that the solutions by the approach are better than that of solutions available in the literature. Finally statistical simulation has been performed for supremacy the approach.

      PubDate: 2015-06-04T12:04:51Z
  • Improved group search optimization algorithm for coordination of
           directional overcurrent relays
    • Abstract: Publication date: Available online 21 May 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Manijeh Alipour , Saeed Teimourzadeh , Heresh Seyedi
      In this study, coordination of directional overcurrent relays in power systems is formulated as an optimization problem. The objective is to find out an optimum setting of relays in order to minimize the operating time of relays for faults at their primary protection zone, while coordinating the relays properly. The coordination is performed using Improved Group Search Optimization algorithm. This paper introduces IGSO by applying some modifications to the original GSO in order to improve its searching ability. In order to validate the efficiency of the proposed IGSO, comprehensive simulation studies are carried out. The simulation studies include some benchmark test functions and test power systems. The results are compared with some existing analytic and evolutionary methods. Numerical results confirm efficiency of the proposed method in comparison with some recently published papers.

      PubDate: 2015-05-22T09:41:52Z
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: June 2015
      Source:Swarm and Evolutionary Computation, Volume 22

      PubDate: 2015-05-12T16:48:00Z
  • Hybridizing genetic algorithm with differential evolution for solving the
           unit commitment scheduling problem
    • Abstract: Publication date: Available online 4 May 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Anupam Trivedi , Dipti Srinivasan , Subhodip Biswas , Thomas Reindl
      This paper proposes a hybrid of genetic algorithm (GA) and differential evolution (DE), termed hGADE, to solve one of the most important power system optimization problems known as the unit commitment (UC) scheduling. The UC problem is a nonlinear mixed-integer combinatorial high-dimensional and highly constrained optimization problem consisting of both binary UC variables and continuous power dispatch variables. Although GA is more capable of efficiently handling binary variables, the performance of DE is more remarkable in real parameter optimization. Thus, in the proposed algorithm hGADE, the binary UC variables are evolved using GA while the continuous power dispatch variables are evolved using DE. Two different variants of hGADE are presented by hybridizing GA with two classical variants of DE algorithm. Additionally, in this paper a problem specific heuristic initial population generation method and a replacement strategy based on preservation of infeasible solutions in the population is incorporated to enhance the search capability of the hybridized variants on the UC problem. The scalability of the proposed algorithm hGADE is demonstrated by testing on systems with generating units in range of 10 up to 100 in one-day scheduling period and the simulation results demonstrate that hGADE algorithm can provide system operator with remarkable cost savings as compared to the best approaches in literature. Finally, an ensemble optimizer based on combination of hGADE variants is implemented to further amplify the performance of the presented algorithm.

      PubDate: 2015-05-07T16:27:43Z
  • Chaos driven discrete artificial bee algorithm for location and assignment
           optimisation problems
    • Abstract: Publication date: Available online 24 March 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Magdalena Metlicka , Donald Davendra
      The chaos driven discrete artificial bee colony (CDABC) algorithm is introduced in this paper. Four unique chaos maps of Burgers, Lozi, Delayed Logistic and Tinkerbell are embedded as chaos pseudo-random number generators and compared with the Mersenne Twister pseudo-random number generator. Two unique problems of quadratic assignment and capacitated vehicle routing problem are solved using five different variants of the algorithm and analytical comparison is conducted. Furthermore, paired t-test is done pairwise on all variants, and from these results it is ascertained that the Tinkerbell variant of CDABC is the best performing for both problem classes.

      PubDate: 2015-04-05T04:43:54Z
  • 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
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