面向多模态函数的自适应混沌爬山微粒群算法

An Adaptive Chaotic Hill-climbing Particle Swarm Optimization Algorithm for Multimodal Functions

针对微粒群算法在多模态函数优化中难以找到全部极值点以及陷入局部最优和后期收敛速度慢等缺陷,提出了一种基于熵的自适应混沌爬山微粒群算法.算法根据熵的值来衡量种群多样性,当发现种群多样性匮乏时,采用动态混沌机制增强多样性;后期融入了局部收敛速度较快的爬山算法提高微粒群算法的后期收敛速度.4种典型多模态函数测试结果表明该算法在求解复杂多模态函数优化问题方面的可行性.

An adaptive chaotic hill-climbing particle swarm optimization was presented in order to overcome the unability to find all extreme points, local optimum and slow convergence speed at later time caused by Particle Swarm Optimization （PSO） in multimodal function optimization. An improved PSO was proposed , and the population of di- versity was measured by entropy. A dynamic chaos mechanism was used to increase the diversity when there is a lack of population diversity, and a hill-climbing method was introduced to improve the convergence speed of PSO in later period. Four kinds of typical multimodal functions were chosen to test the performance of the improved algorithm in solving complex multimodal function optimization problems. The results show that the improved algorithm has better performance than the existing algorithms.