湖南大学学报（自然科学版）2013，Vol.40Issue(2)：77-81,5.
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
摘要
Abstract
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 diversity was measured by entropy. A dynamic chads mechanism was. used to increase the diveirsity when ther 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.关键词
微粒群算法/多模态函数/熵/混沌机制/爬山算法Key words
particle swarm optimization/ multi-modal function/ entropy/ chaos mechanism/ hill-climbing分类
信息技术与安全科学引用本文复制引用
张英杰,郭会芳,付海滨,范朝冬..面向多模态函数的自适应混沌爬山微粒群算法[J].湖南大学学报（自然科学版）,2013,40(2):77-81,5.基金项目
国家自然科学基金资助项目,(61174140) （61174140）
湖南省科技计划重点项目(2010GK2022) （2010GK2022）
长沙市科技计划重点项目(K100501811) （K100501811）
