粒子群分形进化算法  

Fractal Evolutionary Particle Swarm Optimization

作　　者：邱晓红[1,2] 邱晓辉[2] 龚姚腾[1] 

机构地区：[1]江西理工大学软件学院,南昌330013 [2]江西农业大学高等教育研究所,南昌330045

出　　处：《模式识别与人工智能》2013年第4期344-350,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60674054);江西省自然科学基金项目(No.2010GZS0090)资助

摘　　要：在传统粒子群优化(PSO)算法的基础上,提出粒子群分形进化算法(FEPSO).FEPSO利用分形布朗运动模型中的无规则运动特性模拟优化目标函数未知特性,隐含的趋势变化模拟优化目标函数极值变化的总趋势,从而克服个体过于随机进化和早熟的现象.与传统的PSO算法相比,文中算法中每个粒子包含分形进化阶段.在分形进化阶段,粒子在解的子空间以不同的分形参数进行分形布朗运动方式搜索解空间,并对其分量进行更新.仿真实验结果表明,该算法对大部分标准复合测试函数都具有较强的全局搜索能力,其性能超过国际上最近提出的基于PSO的改进算法.Based on the classic particle swarm optimization （PSO） algorithm, a fractal evolutionary particle swarm optimization （FEPSO） is proposed . In FEPSO, the charactristic of the irregular motion of fractal Brownian motion model is used to simulate the optimization process varying in unknown mode, and its implied trend part is applied to simulate the optimization index of the global objective function optimum change. Therefore, the individual evolution process is prevented from going too randomly and precociously. Compared with the classic PSO algorithm, a fractal evolutionary phase is included for each particle in FEPSO. In this phase, each particle simulates a fractal Brownian motion with different Hurst parameter to search the solution in sub dimensional space, and its corresponding sub position is updated. The results of simulation experiments show that the proposed algorithm has a robust global search ability for most standard composite test functions and its optimization ability performs better than the recently proposed improved algorithm based on PSO.

关 键 词：进化算法 粒子群优化 分形布朗运动 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]