Integrated the simplified interpolation and clonal selection into the particle swarm optimization for optimization problems

Authors:
Jing Wang;Xiaohua Zhang;Licheng Jiao
Affiliations:
Institute of Intelligent Information Processing and National Key Laboratory for Radar Signal Processing, Xidian University, Xi'an, Shaanxi, China;Institute of Intelligent Information Processing and National Key Laboratory for Radar Signal Processing, Xidian University, Xi'an, Shaanxi, China;Institute of Intelligent Information Processing and National Key Laboratory for Radar Signal Processing, Xidian University, Xi'an, Shaanxi, China
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

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Abstract

Particle Swarm Optimization (PSO) is gaining momentum as a simple and effective optimization technique. However, its performance on complex problem with multiple minima falls short of that of the Ant Clony Optimization (ACO) algorithm. The new algorithm, which we call Hybrid Particle Swarm Optimization, combines the ideas of particle swarm optimizati-on with clonal selection strategy and simplified quadratic interpolation (SQI), which is used to improve its local search ability, and to escape from the local optima. Simulation results on 14 benchmark test functions show that the hybrid algorithm is able to avoid the premature convergence and find much better solutions with high speed.