Particle swarm optimizer based on small-world topology and comprehensive learning

Authors:
Yanmin Liu;Dongshen Luo;Qingzhen Zhao;Changling Sui
Affiliations:
Department of math, and Zunyi Normal College and College of Management and Economics, Shandong Normal University;Department of math, and Zunyi Normal College;Department of math, and Zunyi Normal College;College of Management and Economics, Shandong Normal University
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

Citing 3
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Abstract

PSO may easily get trapped in a local optimum, when solving complex multimodal problems. In this paper, an improved PSO based on small world network and comprehensive is proposed. The learning exemplar of each particle includes three parts: the global best particle, its own best particle (pbest) and the pbest of its neighborhood. And a random position around itself is needed to increase a probability to jump to that promising region. These strategies enable the diversity of the swarm to be preserved to discourage premature convergence. In benchmark function test, the SCPSO algorithm achieves better solutions than other PSOs.