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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  [2801 journals]
  • A distributed neuro-genetic programming tool
    • Abstract: Publication date: Available online 23 November 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Marco Russo
      This paper describes the performance of the Brain Project, a distributed software tool for the formal modeling of numerical data using a hybrid neural-genetic programming technique. One of the most interesting characteristics of the Brain Project is its distributed implementation. Unlike many other parallel and/or distributed solutions the only requirement of the Brain Project is that the collaborating personal computers must be 64-bit Linux machines connected to Internet via the transmission control protocol/internet protocol. The performance of the Brain Project is clearly enhanced with the very simple parallelization scheme illustrated in the paper. Although the Brain Project presents many innovative solutions for the genetic programming research, this paper focuses mainly on its behavior in the distributed environment.

      PubDate: 2015-11-26T18:23:17Z
  • Recent advances in swarm and evolutionary computation-foreword
    • Abstract: Publication date: Available online 17 November 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Ivan Zelinka, Jouni Lampinen, Vaclav Snášel, Roman Šenkeřík

      PubDate: 2015-11-21T17:58:11Z
  • Benchmarking NLopt and state-of-the-art algorithms for Continuous Global
           Optimization via IACOR
    • Abstract: Publication date: Available online 22 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Udit Kumar, Sumit Soman, Jayadeva
      This paper presents a comparative analysis of the performance of the Incremental Ant Colony algorithm for continuous optimization ( IACO R ), with different algorithms provided in the NLopt library. The key objective is to understand how various algorithms in the NLopt library perform in combination with the Multi Trajectory Local Search (Mtsls1) technique. A hybrid approach has been introduced for the local search strategy, by the use of a parameter that allows for probabilistic selection between Mtsls1 and the NLopt algorithm. In case of stagnation, a switch is made based on the algorithm being used in the previous iteration. This paper presents an exhaustive comparison on the performance of these approaches on Soft Computing (SOCO) and Congress on Evolutionary Computation (CEC) 2014 benchmarks. For both sets of benchmarks, we conclude that the best performing algorithm is a hybrid variant of Mtsls1 with BFGS for local search.

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

      PubDate: 2015-10-27T18:25:25Z
  • Analysis of particle swarm optimization based hierarchical data clustering
    • Abstract: Publication date: Available online 23 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Shafiq Alam, Gillian Dobbie, Saeed Ur Rehman
      Data clustering is one of the most widely used data mining techniques, classifying similar data items into groups on the basis of similarity among the data items. Different issues have been observed while achieving the classification of data into the most suitable grouping. Efficiency of the clustering techniques and accuracy of the resulting groups are two of the main issues. To tackle these issues, recently, optimization based techniques have been used, resulting in enhanced quality of the output and improved efficiency of the clustering process. Swarm Intelligence (SI) is one such technique whose different algorithms have been found effective for this purpose. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are the two most prominent SI based techniques. In this paper we analyze the use of PSO for data clustering in particular for clustering in a hierarchical manner. We chose PSO based hierarchical techniques, Evolutionary PSO for clustering (EPSO-clustering) and Hierarchical PSO for clustering (HPSO-clustering). Both these techniques work in a hierarchical agglomerative manner, with HPSO-clustering an extension of EPSO-clustering. It combines the properties of hierarchical and partitional clustering and adds SI based optimization to the process. We evaluate our proposed clustering techniques on different benchmark datasets from UCI machine learning data repository as well as real data that we collected locally from a web server. We used inter-cluster and intra-cluster distances, and execution time to measure the performance of our proposed techniques. For evaluation we selected different clustering techniques that were previously used as benchmarks such as k-means, PSO-clustering, Hierarchical Agglomerative Clustering (HAC) and DBSCAN. The results verify that the proposed techniques perform better on the suggested measures against the benchmarks mentioned.

      PubDate: 2015-10-27T18:25:25Z
  • Efficient visualization of social networks based on modified
           Sammon׳s mapping
    • Abstract: Publication date: Available online 23 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Milos Kudelka, Pavel Kromer, Martin Radvansky, Zdenek Horak, Vaclav Snasel
      Visualization is an important part of Network Analysis. It helps to find features of the network that are not easily identifiable. In this paper, we present a novel approach to the visualization of weighted networks based on the Distance Geometry Problem. The network may be seen as a set of data points in space induced by the incidence relation or as a symmetric matrix of vertex distances. We propose two methods for construction of the input for Sammon׳s mapping and discuss the effect of the particular methods on the final layout. In this work, we use Differential Evolution as a real-parameter optimization metaheuristic algorithm to minimize the error function used in Sammon׳s mapping. The presented experiments used the well-known Zachary׳s Karate Club network and weighted co-authors network based on the DBLP database. We present our approach to the visualization of weighted networks based on Sammon׳s mapping and linear approximation. Dimensionality reduction and graph based visualization methods can uncover hidden structures of high dimensional data and visualize it in a low-dimensional vector space.

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

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

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

      PubDate: 2015-10-22T18:16:57Z
  • Self-adaptive control parameters' randomization frequency and propagations
           in differential evolution
    • Abstract: Publication date: Available online 17 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Aleš Zamuda, Janez Brest
      This paper presents insight into an adaptation and self-adaptation mechanism within differential evolution, covering not only how but moreover – when this mechanism generates new values for control parameters, focusing on the iteration-temporal randomness of the self-adaptive control parameters. In particular, this randomness is controlled by a randomness level parameter, which influences the control parameters values' dynamics and their propagation through suitable individuals' improvement contributions during ellitistic selection. Thereby, the randomness level parameter defines the chaotic behavior of self-adaptive control parameter values' instances. A Differential Evolution (DE) algorithm for Real Parameter Single Objective Optimization is utilized as an application of this mechanism, to analyze the impact of the randomness level parameter as used inside the evolutionary algorithm parameter adaptation and control mechanism, yielding statistically significant different algorithm performances and ranks on different randomness level parameter values. Moreover, the impacts of different randomness configurations on the number of improvements, improvement scales, and adaptation frequencies, are shown, in order to present a deeper insight into the influences and causes using different randomness level parameter configurations, to present the influence of randomization frequency on propagation stability. Since DE variant algorithms with the mechanism of control parameters self-adaptation are widely applied, this study might help in increasing the performances of these different variants and their applications.

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

      PubDate: 2015-10-22T18:16:57Z
    • Abstract: Publication date: Available online 21 October 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Michal Pluhacek, Roman Senkerik, Donald Davendra
      In this study the Chaotic Particle Swarm Optimization (CPSO) algorithm with six simultaneously used chaotic pseudo-random number generators (CPRNG) is investigated. The implementation of chaotic sequences is detailed and discussed. The ensemble learning approach is used for assigning CPRNGs to particles. The results of proposed algorithm on IEEE CEC´13 Real-Parameter Single Objective Optimization benchmark set are presented and compared with the SPSO-2011.

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

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

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

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

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

      PubDate: 2015-09-27T16:59:49Z
  • Review of Nature-inspired Methods for Wake-up Scheduling in Wireless
           Sensor Networks
    • Abstract: Publication date: Available online 14 September 2015
      Source:Swarm and Evolutionary Computation
      Author(s): P. Musilek, P. Kromer, T. Barton
      Over the last few decades, algorithms inspired by nature have matured into a widely used class of computing methods. They have shown the ability to adjust to variety of conditions, and have been frequently employed for solving complex, real-world optimization problems. They are especially suitable for problems that require adaptation, and that involve optimization of complex, distributed systems, operating in dynamic environments. Among other application domains, nature-inspired methods have been extensively used in the areas of networking in general, and wireless sensor networks in particular. Energy management and network lifetime optimization are two great research and implementation challenges for wireless sensor networks. Duty cycle management, synchronization, and wake-up scheduling are complementary approaches that facilitate this complex optimization process. This review focuses on the intersection of nature-inspired computing and wake-up scheduling algorithms for wireless sensor networks. It describes the state-of-the-art in these fields and provides an up-to-date review of the most recent developments in this interdisciplinary domain. It discusses the motivation for using nature-inspired methods for wake-up scheduling, and presents related open issues and research challenges.

      PubDate: 2015-09-18T09:37:38Z
  • Opposition-Based Magnetic Optimization Algorithm With Parameter Adaptation
    • Abstract: Publication date: Available online 16 September 2015
      Source:Swarm and Evolutionary Computation
      Author(s): Mahdi Aziz, Mohammad - H. Tayarani- N.
      Magnetic Optimization Algorithm (MOA) has emerged as a promising optimization algorithm that is inspired by the principles of magnetic field theory. In this paper we improve the performance of the algorithm in two aspects. First an Opposition-Based Learning (OBL) approach is proposed for the algorithm which is applied to the movement operator of the algorithm. Second, by learning from the algorithm's past experience, an adaptive parameter control strategy which dynamically sets the parameters of the algorithm during the optimization is proposed. To show the significance of the proposed parameter adaptation strategy, we compare the algorithm with two well-known parameter setting techniques on a number of benchmark problems. The results indicate that although the proposed algorithm with the adaptation strategy does not require to set the parameters of the algorithm prior to the optimization process, it outperforms MOA with other parameter setting strategies in most large-scale optimization problems. We also study the algorithm while employing the OBL by comparing it with the original version of MOA. Furthermore, the proposed algorithm is tested and compared with seven traditional population-based algorithms and eight state-of-the-art optimization algorithms. The comparisons demonstrate that the proposed algorithm outperforms the traditional algorithms in most benchmark problems, and its results is comparative to those obtained by the state-of-the-art algorithms.

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

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

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

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

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

      PubDate: 2015-08-14T05:56:36Z
  • 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
  • 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
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