Design and Simulation of an Improved Particle Swarm Optimization

Abstract:

The particle swarm optimization (PSO) algorithm is a new population search strategy, which has exhibited good performance through well-known numerical test problems. However, it is easy to trap into local optimum because the population diversity becomes worse during the evolution. In order to overcome the shortcoming of the PSO, this paper proposes an improved PSO based on the symmetry distribution of the particle space position. From the research of particle movement in high dimensional space, we can see: the more symmetric of the particle distribution, the bigger probability can the algorithm be during converging to the global optimization solution. A novel population diversity function is put forward and an adjustment algorithm is put into the basic PSO. The steps of the proposed algorithm are given in detail. With two typical benchmark functions, the experimental results show the improved PSO has better convergence precision than the basic PSO.

• Periodical: Applied Mechanics and Materials (Volumes 20-23)
• Pages: 1280-1285
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.20-23.1280
• Online since: January 2010
• Authors: Jian Xiang Wei, Yue Hong Sun
• Keywords: Diversity Adjustment, Particle Swarm Optimization Algorithm (PSO), Symmetrical Distribution

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