多核环境下并行粒子群算法

doi:10.11772/j.issn.1001-9081.2015.09.2482

计算机应用 ›› 2015, Vol. 35 ›› Issue (9): 2482-2485.DOI: 10.11772/j.issn.1001-9081.2015.09.2482

多核环境下并行粒子群算法

何莉¹, 刘晓东¹, 李松阳¹, 张倩²

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

收稿日期:2015-04-15 修回日期:2015-06-11 出版日期:2015-09-10 发布日期:2015-09-17
通讯作者: 何莉(1982-),男,江西萍乡人,讲师,博士,CCF会员,主要研究方向:智能计算、物联网,engineerheli@126.com
作者简介:刘晓东(1981-),男,河南林州人,讲师,博士,主要研究方向:云计算、并行计算;李松阳(1985-),男,河南南阳人,讲师,博士,主要研究方向:群体智能、虚拟植物;张倩(1982-),女,河南驻马店人,讲师,博士,主要研究方向:晶体合成、物联网。
基金资助:
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)。

Parallel particle swarm optimization algorithm in multicore computing environment

HE Li¹, LIU Xiaodong¹, LI Songyang¹, ZHANG Qian²

1. School of Computer, Henan Institute of Engineering, Zhengzhou Henan 451191, China;
2. School of Materials and Chemical Engineering, Henan Institute of Engineering, Zhengzhou Henan 451191, China

Received:2015-04-15 Revised:2015-06-11 Online:2015-09-10 Published:2015-09-17

摘要/Abstract

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。

关键词: 并行粒子群算法, 多核计算, 并行算法, 最优化, 群智能

Abstract: 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 PSO, and expand applied range of PSO.

Key words: parallel Particle Swarm Optimization (PSO), multicore computing, parallel algorithm, optimization, swarm intelligence

中图分类号:

TP301.6

何莉, 刘晓东, 李松阳, 张倩. 多核环境下并行粒子群算法[J]. 计算机应用, 2015, 35(9): 2482-2485.

HE Li, LIU Xiaodong, LI Songyang, ZHANG Qian. Parallel particle swarm optimization algorithm in multicore computing environment[J]. Journal of Computer Applications, 2015, 35(9): 2482-2485.

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多核环境下并行粒子群算法

Parallel particle swarm optimization algorithm in multicore computing environment

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