多核环境下并行粒子群算法  

Parallel particle swarm optimization algorithm in multicore computing environment

作　　者：何莉[1] 刘晓东[1] 李松阳[1] 张倩[2] 

机构地区：[1]河南工程学院计算机学院,郑州451191 [2]河南工程学院材料与化学工程学院,郑州451191

出　　处：《计算机应用》2015年第9期2482-2485,2518,共5页journal of Computer Applications

基　　金：国家自然科学基金青年项目(61301232);河南省教育厅科学技术研究重点项目(13A520148);河南工程学院博士基金资助项目(D2012016)

摘　　要：针对串行粒子群算法在解决大任务耗时过长的问题,提出一种共享并行粒子群(Shared-PSO)算法。充分利用多核处理能力缩短问题处理运行时间,设置共享区和采取粒子随机替换策略有效促进粒子信息的交流,其算法流程具有较好的通用性,允许利用多种串行粒子群算法完成粒子信息更新工作。在标准优化测试集CEC 2014上的实验结果显示新算法的执行时间是串行算法的1/4。新算法能够有效地改善串行粒子群的执行效率,扩展粒子群算法的应用范围。Aiming at the problem that serial Particle Swarm Optimization （PSO） algorithms are time-consuming to deal with big tasks, a novel shared parallel PSO （Shared-PSO） algorithm was proposed. The multi-core processing power was used to reduce time to get resolution. In order to facilitate communication of particles, a shared area was set up and a random strategy was applied to switch particles. Several serial PSO algorithms could be permitted to update particle information because of the universality of its algorithm flow. Shared-PSO was applied on the standard optimization test set CEC （ Congress on Evolutionary Computation） 2014. The experiment results show that the execution time of Shared-PSO is a quarter of the serial PSO＇s. The proposed algorithm can effectively improve the execution efficiency of serial PS0, and expand applied range of PSO.

关 键 词：并行粒子群算法 多核计算 并行算法 最优化 群智能 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]