Multi-swarm particle swarm optimization based on mixed search behavior

The paper develops a Multi-swarm particle swarm optimization (MPSO) to overcome the premature convergence problem. MPSO takes advantage of multiple sub-swarms with mixed search behavior to maintain the swarm diversity, and introduces cooperative mechanism to prompt the information exchange among sub...

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Bibliographic Details
Published in2010 5th IEEE Conference on Industrial Electronics and Applications pp. 605 - 610
Main Authors Jing Jie, Wanliang Wang, Chunsheng Liu, Beiping Hou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Algorithm design and analysis
Birds
Computer science
Convergence
coperative search
Cultural differences
Educational institutions
Maintenance engineering
Paper technology
Particle swarm optimization
Robustness
swarm diversity
swarm intelligence
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Summary:The paper develops a Multi-swarm particle swarm optimization (MPSO) to overcome the premature convergence problem. MPSO takes advantage of multiple sub-swarms with mixed search behavior to maintain the swarm diversity, and introduces cooperative mechanism to prompt the information exchange among sub-swarms. Moreover, MPSO adopts an adaptive reinitializing strategy guided by swarm diversity, which can contribute to the global convergence of the algorithm. Through the mixed local search behavior modes, the cooperative search and the reinitializing strategy guided by swarm diversity, MPSO can maintain appropriate diversity and keep the balance of local search and global search validly. The proposed MPSO was applied to some well-known benchmarks. The experimental results show MPSO is a robust global optimization technique for the complex multimodal functions.
ISBN:1424450454
9781424450459
ISSN:2156-2318
2158-2297
DOI:10.1109/ICIEA.2010.5517044