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Second Generation Particle Swarm Optimization

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1 Author(s)
Mingquan Chen ; School of Computer and Communication, Hunan University, ChangSha City, 410012 in China

Second generation particle swarm optimization (SGPSO) is a new swarm intelligence optimization algorithm. SGPSO is based on the PSO. But the SGPSO will sufficiently utilize the information of the optimum swarm. The optimum swarm consists of the local optimum solution of every particle. In the SGPSO, every particle in the swarm not only moves to the local optimum solution and the global optimum solution, but also moves to the geometric center of optimum swarm. SGPSO, PSO and PSO with time-varying acceleration coefficients(PSO TVAC) are compared on some benchmark functions. And experiment results show that SGPSO performs better in the accuracy and in getting rid of the premature than PSO and PSO_TVAC. And according to the different swarm centers which every particle moves to, I will show some kinds of the variation of SGPSO.

Published in:

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

Date of Conference:

1-6 June 2008