关键词: 粒子群优化 文化算法 模糊知识表示 自适应惯性权重

Abstract:

The Particle Swarm Optimization （PSO） is a population-based evolution algorithm and the inertia weight plays a key role in PSO. In this paper, a novel elass of adaptive PSO is proposed based on the Cultural Algorithm （CA）. The fuzzy rules represent the experienees of the particles, and are shared  in the population to form the eulture. When the population is evolving, the eulture is evolved by the Genetie Algorithm （GA）. The fuzzy systems, whicch are eonstrueted by the fuzzy rules in the belief spaee, approximate to the controller of inertia weight whieh is the fittest controller to the partieular problem The simulation results illustrate that the PSO using CA with fuzzy knowledge evolution is a promising optimization algorithm.

Key words: particle swarm optimization, cultural algorithm, fuzzy knowledge evolution, adaptive inertia weight