An Improved PSO with Small-World Topology and Comprehensive Learning

Yanmin Liu and Ben Niu
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Yanmin Liu: School of Mathematics and Computer Science, Zunyi Normal College, Zunyi, China
Ben Niu: School of Economics and Management, Tongji University, Shanghai, China

International Journal of Swarm Intelligence Research (IJSIR), 2014, vol. 5, issue 2, 13-28

Abstract: Particle swarm optimization (PSO) is a heuristic global optimization method based on swarm intelligence, and has been proven to be a powerful competitor to other intelligent algorithms. However, PSO may easily get trapped in a local optimum when solving complex multimodal problems. To improve PSO's performance, in this paper the authors propose an improved PSO based on small world network and comprehensive learning strategy (SCPSO for short), in which the learning exemplar of each particle includes three parts: the global best particle (gbest), personal best particle (pbest), and the pbest of its neighborhood. Additionally, a random position around a particle is used to increase its probability to jump to a promising region. These strategies enable the diversity of the swarm to discourage premature convergence. By testing on five benchmark functions, SCPSO is proved to have better performance than PSO and its variants. SCPSO is then used to determine the optimal parameters involved in the Van-Genuchten model. The experimental results demonstrate the good performance of SCPSO compared with other methods.

Date: 2014
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