计算机应用研究2016，Vol.33Issue(12)：3643-3647,5.DOI:10.3969/j.issn.1001-3695.2016.12.028
基于遗传交叉和多混沌策略改进的粒子群优化算法
Improved particle swarm optimization algorithm based on genetic crossover and multi-chaotic strategies
摘要
Abstract
This paper proposed a particle swarm optimization(PSO)algorithm based on genetic crossover and multi-chaotic strategies to effectively improve the search abilities of the basic PSO algorithm.The proposed algorithm used four measurements to obtain the better solution than the optimal solution of the current swarm.The first was to perform a genetic crossover opera-tion between the optimal solution of the current swarm and the best solution of each particle.The second was that a chaotic sys-tem dynamically adjusted the inertia weight.The third was a chaotic global search for the whole solution space.The last was to perform a multi-dimensional and single-dimensional chaotic local search on the optimal solution of the current swarm.Simula-tion results show that compared with the other three algorithms,the proposed algorithm not only has the fastest convergence speed,but also has a 100% success rate when solving eight integer and mixed integer nonlinear programming problems.关键词
粒子群优化算法/遗传交叉/混沌惯性权重/多维和单维混沌局部搜索/混沌全局搜索Key words
particle swarm optimization/genetic crossover/chaotic inertia weight/multi-dimensional and single-dimension chaotic local search/chaotic global search分类
信息技术与安全科学引用本文复制引用
谭跃,谭冠政,邓曙光..基于遗传交叉和多混沌策略改进的粒子群优化算法[J].计算机应用研究,2016,33(12):3643-3647,5.基金项目
国家自然科学基金资助项目（61471164）；湖南省科技计划资助项目（2014FJ3112）；湖南省教育厅优秀青年资助项目 （）
