Particle Swarm Optimization with Adaptive Parameters

Authors:
Dongyong Yang;Jinyin Chen;Naofumi Matsumoto
Affiliations:
Zhejiang University of Technology, China;Zhejiang University of Technology, China;Ashikaga Institute of Technology, Japan
Venue:
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01
Year:
2007

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Abstract

Particle swarm optimization is an effective evolution algorithm for global optimizing. Based on analysis of particle movements during evolution, parameter p is brought up to control the value of C1 and C2, which effects convergence rate of PSO. Aiming at solving different problems, corresponding p is adopted to improve performance. Particle confidence coefficient q is applied to weigh proper emphasize on itself best solution and global solution. Adaptive value of q is introduced to PSO to satisfy specific situation for each particle. Finally, performance of PSO with parameters p and q is testified by optimizing benchmark functions.