research-article

A hybrid particle swarm optimization approach with prior crossover differential evolution

Authors:

Xingsheng GuAuthors Info & Claims

GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation

Pages 671 - 678

https://doi.org/10.1145/1543834.1543926

Published: 12 June 2009 Publication History

Abstract

Particle swarm optimization (PSO) is population-based heuristic searching algorithm. PSO has excellent ability of global optimization. However, there are some shortcomings of prematurity, low convergence accuracy and speed, similarly to other evolutionary algorithms (EA). To improve its performance, a hybrid particle swarm optimization is proposed in the paper. Firstly, the average position and velocity of particles are incorporated into basic PSO for concerning with the effect of the evolution of the whole swarm. Then a differential evolution (DE) computation, which introduces an extra population for prior crossover, is hybridized with the improved PSO to form a novel optimization algorithm, PSOPDE. The role of prior crossover is to appropriately diversify the population and increase the probability of reaching better solutions. DE component takes into account the stochastic differential variation, and enhances the exploitation in the neighborhoods of current solutions. PSOPDE is implemented on five typical benchmark functions, and compared with six other algorithms. The results indicate that PSOPDE behaves better, and greatly improve the searching efficiency and quality.

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Cited By

Moharam AEl-Hosseini MAli H(2016)Design of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengersApplied Soft Computing10.1016/j.asoc.2015.10.04138:C(727-737)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.asoc.2015.10.041
Parouha RDas K(2016)A memory based differential evolution algorithm for unconstrained optimizationApplied Soft Computing10.1016/j.asoc.2015.10.02238:C(501-517)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.asoc.2015.10.022
Parouha RDas K(2015)Parallel hybridization of differential evolution and particle swarm optimization for constrained optimization with its applicationInternational Journal of System Assurance Engineering and Management10.1007/s13198-015-0354-67:S1(143-162)Online publication date: 4-Apr-2015
https://doi.org/10.1007/s13198-015-0354-6
Show More Cited By

Index Terms

A hybrid particle swarm optimization approach with prior crossover differential evolution
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

cover image ACM Conferences

GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation

June 2009

1112 pages

ISBN:9781605583266

DOI:10.1145/1543834

General Chairs:
Lihong Xu
Tongji University, China
,
Erik D. Goodman
Michigan State University, USA
,
Program Chairs:
Guoliang Chen
University of Sci. & Tech. of China
,
Darrell Whitley
Colorado State University, USA
,
Yongsheng Ding
Donghua University, China

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 June 2009

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GEC '09: GEC '09 - ACM/SIGEVO Genetic and Evolutionary Computation Summit

June 12 - 14, 2009

Shanghai, China

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Cited By

Moharam AEl-Hosseini MAli H(2016)Design of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengersApplied Soft Computing10.1016/j.asoc.2015.10.04138:C(727-737)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.asoc.2015.10.041
Parouha RDas K(2016)A memory based differential evolution algorithm for unconstrained optimizationApplied Soft Computing10.1016/j.asoc.2015.10.02238:C(501-517)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.asoc.2015.10.022
Parouha RDas K(2015)Parallel hybridization of differential evolution and particle swarm optimization for constrained optimization with its applicationInternational Journal of System Assurance Engineering and Management10.1007/s13198-015-0354-67:S1(143-162)Online publication date: 4-Apr-2015
https://doi.org/10.1007/s13198-015-0354-6
Ali MPant MAbraham A(2012)Improving differential evolution algorithm by synergizing different improvement mechanismsACM Transactions on Autonomous and Adaptive Systems10.1145/2240166.22401707:2(1-32)Online publication date: 30-Jul-2012
https://dl.acm.org/doi/10.1145/2240166.2240170
Xin BChen JZhang JFang HPeng Z(2012)Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful OptimizersIEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews10.1109/TSMCC.2011.216094142:5(744-767)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1109/TSMCC.2011.2160941
Sun YZhang LGu X(2012)A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problemsNeurocomputing10.1016/j.neucom.2011.08.04398(76-89)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1016/j.neucom.2011.08.043
Abdullah ADeris SHashim SMohamad MArjunan S(2011)An improved local best searching in Particle Swarm Optimization using Differential Evolution2011 11th International Conference on Hybrid Intelligent Systems (HIS)10.1109/HIS.2011.6122090(115-120)Online publication date: Dec-2011
https://doi.org/10.1109/HIS.2011.6122090
Thangaraj RPant MAbraham ABouvry P(2011)Particle swarm optimization: Hybridization perspectives and experimental illustrationsApplied Mathematics and Computation10.1016/j.amc.2010.12.053217:12(5208-5226)Online publication date: Feb-2011
https://doi.org/10.1016/j.amc.2010.12.053
Ali MPant M(2011)Improving the performance of differential evolution algorithm using Cauchy mutationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-010-0655-215:5(991-1007)Online publication date: 1-May-2011
https://dl.acm.org/doi/10.1007/s00500-010-0655-2
Yang Sun Lingbo Zhang Xingsheng Gu (2010)Membrane computing based particle swarm optimization algorithm and its application2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)10.1109/BICTA.2010.5645198(631-636)Online publication date: Sep-2010
https://doi.org/10.1109/BICTA.2010.5645198
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