Growing Particle Swarm Optimizers with a Population-Dependent Parameter

Authors:
Chihiro Kurosu;Toshimichi Saito;Kenya Jin'No
Affiliations:
Hosei University, Tokyo, Japan 184-8584;Hosei University, Tokyo, Japan 184-8584;Nippon Institute of Technology, Saitama, Japan 345-8501
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Year:
2009

Citing 12
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Abstract

This paper studies a new version of growing particle swarm optimizers. In the algorithm, a new particle is born if a particle exploring the optimum is stagnated and the swarm can grow depending on problem complexity. The particle velocity is controlled by an acceleration parameter that can attenuate depending on the number of particles and can vibrate depending on the time. The parameter plays important role to reduce the computation cost and to increase the success rate. The algorithm efficiency is confirmed by numerical experiments of typical benchmarks.