Multi-Exemplar Particle Swarm Optimization
PSO and its variants have proven to be useful algorithms for tackling a wide range of optimization problems in recent decades. However, PSO and most of its variants only consider the influences caused by global best position and personal historical best position. Such a single way of influencing oft...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9205229/ |
id |
doaj-66e7265022ad486088e6f01caf3b0811 |
---|---|
record_format |
Article |
spelling |
doaj-66e7265022ad486088e6f01caf3b08112021-03-30T04:02:13ZengIEEEIEEE Access2169-35362020-01-01817636317637410.1109/ACCESS.2020.30266209205229Multi-Exemplar Particle Swarm OptimizationWei Song0https://orcid.org/0000-0002-3148-5827Ziyu Hua1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, ChinaPSO and its variants have proven to be useful algorithms for tackling a wide range of optimization problems in recent decades. However, PSO and most of its variants only consider the influences caused by global best position and personal historical best position. Such a single way of influencing often leads to an issue of insufficient diversity of population, and further makes the algorithms be prone to falling into local optimum. In this paper, we propose a multi-exemplar particle swarm optimization (MEPSO) to deal with this issue. Specifically, each particle will choose the global best particle and the best companion particle as its exemplars, which brings more useful knowledge for particle update. To further describe the influences with respect to different exemplars, we define two influence coefficients inspired by mechanics. Such influence coefficients ensure that the best current experience is shared while enrich the diversity of population. Moreover, in the light of the distance between each particle and its best companion particle on each dimension, a variable-scale search is given in this paper to enhance the overall convergence ability. To verify the effectiveness of our algorithm, we conduct abundant experiments on all functions of CEC2013 test suite. The experimental results show that MEPSO performs better than 14 competitors in terms of comprehensive performance and achieves state-of-the-art results.https://ieeexplore.ieee.org/document/9205229/Particle swarm optimizationmulti-exemplarinfluence coefficientvariable-scale search |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Song Ziyu Hua |
spellingShingle |
Wei Song Ziyu Hua Multi-Exemplar Particle Swarm Optimization IEEE Access Particle swarm optimization multi-exemplar influence coefficient variable-scale search |
author_facet |
Wei Song Ziyu Hua |
author_sort |
Wei Song |
title |
Multi-Exemplar Particle Swarm Optimization |
title_short |
Multi-Exemplar Particle Swarm Optimization |
title_full |
Multi-Exemplar Particle Swarm Optimization |
title_fullStr |
Multi-Exemplar Particle Swarm Optimization |
title_full_unstemmed |
Multi-Exemplar Particle Swarm Optimization |
title_sort |
multi-exemplar particle swarm optimization |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
PSO and its variants have proven to be useful algorithms for tackling a wide range of optimization problems in recent decades. However, PSO and most of its variants only consider the influences caused by global best position and personal historical best position. Such a single way of influencing often leads to an issue of insufficient diversity of population, and further makes the algorithms be prone to falling into local optimum. In this paper, we propose a multi-exemplar particle swarm optimization (MEPSO) to deal with this issue. Specifically, each particle will choose the global best particle and the best companion particle as its exemplars, which brings more useful knowledge for particle update. To further describe the influences with respect to different exemplars, we define two influence coefficients inspired by mechanics. Such influence coefficients ensure that the best current experience is shared while enrich the diversity of population. Moreover, in the light of the distance between each particle and its best companion particle on each dimension, a variable-scale search is given in this paper to enhance the overall convergence ability. To verify the effectiveness of our algorithm, we conduct abundant experiments on all functions of CEC2013 test suite. The experimental results show that MEPSO performs better than 14 competitors in terms of comprehensive performance and achieves state-of-the-art results. |
topic |
Particle swarm optimization multi-exemplar influence coefficient variable-scale search |
url |
https://ieeexplore.ieee.org/document/9205229/ |
work_keys_str_mv |
AT weisong multiexemplarparticleswarmoptimization AT ziyuhua multiexemplarparticleswarmoptimization |
_version_ |
1724182415852699648 |