双群分段交换的改进微粒群优化算法研究  

Study on improved particle swarm optimization algorithm based on two sub-swarms multi-phase exchange

作　　者：柳枝华[1] 孙辉[1,2] 胡海智[1] 

机构地区：[1]南昌航空大学计算机学院,南昌330063 [2]南昌工程学院计算机科学与技术系,南昌330099

出　　处：《计算机工程与应用》2010年第25期34-36,56,共4页Computer Engineering and Applications

基　　金：国家自然科学基金No.50539020;江西省自然科学基金No.2007GZS1056;江西教育厅科技项目(No.赣教技字[2007]339号)~~

摘　　要：针对微粒优化算法在高维复杂函数寻优上容易陷入局部极值的问题,提出了一种双群分段交换的改进微粒群优化算法(TSME-PSO)。算法将群体分成规模相同的两个种群,两分群采用不同的进化模型更新微粒的位置与速度。算法搜索的不同阶段,交换不同数目的微粒,且数量是不断减少的。通过这些方法,可以有效地提高种群多样性,增强微粒寻优活力。仿真实验表明,TSME-PSO算法可以有效逃离局部极值,整体寻优性能良好,优于其他算法。Owing to the problem that particle swarm optimization algorithm is easily falling into local optima in optimization of high-dimensional and complicated functions, an improved particle swarm optimization algorithm based on two sub-swarms multi-phase exchange is proposed.The whole particle swarm is divided into two sub-swarms of same size.The models of updating the position and velocity of each population particles are different.The number of exchange particles is different in different searching phases of the algorithm, and the amount is constantly decreasing.With these methods, the population diversity can be improved and the vitality of particles can be enhanced.Results show that TSME-PSO can avoid trapping into local optima effectively and has good ability of searching for global optima, and the overall optimization performance is also better than other comparison algorithms.

关 键 词：微粒群优化算法 局部极值 模型 分段交换 种群多样性 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]