基于模糊文化算法的自适应粒子群优化

为解决粒子群优化中惯性权重的调整机制在具体优化问题中的自适应问题，本文建立了一种全新的基于模糊文化算法的自适应粒子群优化算法；利用模糊规则表示个体粒子在演化过程中获取的经验，经验共享形成群体文化，并利用遗传算法来实现文化的进化；通过信念空间中以模糊规则表示的知识建立模糊系统来逼近与实际问题相适应的惯性权
权重控制器。在测试函数集上的仿真实验对比结果证明，该算法相对于现有算法有优势。

The Adaptive Particle Swarm Optimization Based on the Fuzzy Cultural Algorithm

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.