article

A multi-swarm cooperative multistage perturbation guiding particle swarm optimizer

Authors:

Xiangjun YangAuthors Info & Claims

Applied Soft Computing, Volume 22

Pages 77 - 93

https://doi.org/10.1016/j.asoc.2014.04.042

Published: 01 September 2014 Publication History

Abstract

Inspired by the ideas of multi-swarm information sharing and elitist perturbation guiding a novel multi-swarm cooperative multistage perturbation guiding particle swarm optimizer (MCpPSO) is proposed in this paper. The multi-swarm information sharing idea is to harmoniously improve the evolving efficiency via information communicating and sharing among different sub-swarms with different evolution mechanisms. It is possible to drive a stagnated sub-swarm to revitalize once again with the beneficial information obtained from other sub-swarms. Multistage elitist perturbation guiding strategy aims to slow down the learning speed and intensity in a certain extent from the global best individual while keeping the elitist learning mechanism. It effectively enlarges the exploration domain and diversifies the flying tracks of particles. Extensive experiments indicate that the proposed strategies are necessary and cooperative, both of which construct a promising algorithm MCpPSO when comparing with other particle swarm optimizers and state-of-the-art algorithms. The ideas of central position perturbation along the global best particle, different computing approaches for central position and important parameters influence analysis are presented and analyzed.

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cover image Applied Soft Computing

Applied Soft Computing Volume 22, Issue

September, 2014

696 pages

ISSN:1568-4946

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Copyright © Elsevier B.V. © 2014.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 September 2014

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Cited By

Chen JSong YZhu JWang S(2023)Multi-population Runge Kutta Optimizer Based on Gaussian DisturbanceProceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems10.1145/3609703.3609713(59-64)Online publication date: 28-Jul-2023
https://dl.acm.org/doi/10.1145/3609703.3609713
Sun GLan YZhao R(2019)Self-organizing hierarchical monkey algorithm with time-varying parameterNeural Computing and Applications10.1007/s00521-017-3265-431:8(3245-3263)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s00521-017-3265-4
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https://dl.acm.org/doi/10.1007/s00366-019-00706-3
Xu GLi RHao JZhao XTan Y(2018)A new multi-stage perturbed differential evolution with multi-parameter adaption and directional differenceNatural Computing: an international journal10.1007/s11047-018-9692-z19:4(683-698)Online publication date: 20-Jun-2018
https://dl.acm.org/doi/10.1007/s11047-018-9692-z
Vafashoar RMeybodi M(2018)Multi swarm optimization algorithm with adaptive connectivity degreeApplied Intelligence10.1007/s10489-017-1039-448:4(909-941)Online publication date: 1-Apr-2018
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Zhang QLiu WMeng XYang BVasilakos A(2017)Vector coevolving particle swarm optimization algorithmInformation Sciences: an International Journal10.1016/j.ins.2017.01.038394:C(273-298)Online publication date: 1-Jul-2017
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Yan JHe WJiang XZhang Z(2017)A novel phase performance evaluation method for particle swarm optimization algorithms using velocity-based state estimationApplied Soft Computing10.1016/j.asoc.2017.04.03557:C(517-525)Online publication date: 1-Aug-2017
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Liu JMa DMa TZhang W(2017)Ecosystem particle swarm optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2111-421:7(1667-1691)Online publication date: 1-Apr-2017
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Zhao XLin WHao JZuo XYuan J(2016)Clustering and pattern search for enhancing particle swarm optimization with Euclidean spatial neighborhood searchNeurocomputing10.1016/j.neucom.2015.07.025171:C(966-981)Online publication date: 1-Jan-2016
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