Parallelizing particle swarm optimization

B. Li, K. Wada
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引用次数: 19

Abstract

This paper focuses on a parallel version of particle swarm optimization (PSO) algorithm which can significantly reduces execution time for solving complex large-scale optimization problems. This paper gives an overview of PSO algorithm, and then proposes a design and an implementation of parallel PSO. The proposed algorithm eliminates redundant synchronizations and optimizes message transfer to overlap communication with computation. The experimental results showed that 13.2 times speedup was obtained by the proposed parallel PSO algorithm with 14 processors.
并行粒子群优化
本文研究了一种并行粒子群优化算法(PSO),该算法可以显著缩短求解复杂大规模优化问题的执行时间。本文在概述粒子群算法的基础上,提出了一种并行粒子群算法的设计与实现。该算法消除了冗余同步,优化了消息传递,使通信与计算重叠。实验结果表明,采用14个处理器的并行粒子群算法,速度提高了13.2倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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