Choosing a starting configuration for particle swarm optimization

The performance of particle swarm optimization can be improved by strategically selecting the starting positions of the particles. The work suggests the use of generators from centroidal Voronoi tessellations as the starting points for the swarm. The performance of swarms initialized with this metho...

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Bibliographic Details
Published in2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) Vol. 3; pp. 2309 - 2312 vol.3
Main Authors RICHARDS, Mark, VENTURA, Dan
Format Conference Proceeding
LanguageEnglish
Published Piscataway NJ IEEE 2004
Subjects
Applied sciences
Artificial intelligence
Computer science
Computer science; control theory; systems
Exact sciences and technology
Particle swarm optimization
Stochastic processes
Testing
Initialization
High performance
Voronoï diagram
Evolutionary algorithm
Swarm intelligence
Tiling
Neural network
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ISBN0780383591
9780780383593
ISSN1098-7576
DOI10.1109/IJCNN.2004.1380986

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Summary:The performance of particle swarm optimization can be improved by strategically selecting the starting positions of the particles. The work suggests the use of generators from centroidal Voronoi tessellations as the starting points for the swarm. The performance of swarms initialized with this method is compared with the standard PSO algorithm on several standard test functions. Results suggest that CVT initialization improves PSO performance in high dimensional spaces.
ISBN:0780383591
9780780383593
ISSN:1098-7576
DOI:10.1109/IJCNN.2004.1380986