A speed-up and speed-down strategy for swarm optimization

Haopeng Zhang, Fumin Zhang, Qing Hui
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引用次数: 9

Abstract

In this paper, inspired by speed-up and speed-down (SUSD) mechanism observed by the fish swarm avoiding light, an SUSD strategy is proposed to develop new swarm intelligence based optimization algorithms to enhance the accuracy and efficiency of swarm optimization algorithms. By comparing with the global best solution, each particle adaptively speeds up and speeds down towards the best solution. Specifically, a new directed speed term is added to the original particle swarm optimization (PSO) algorithm or other PSO variations. Due to the SUSD mechanism, the algorithm shows a great improvement of the accuracy and convergence rate compared with the original PSO and other PSO variations. The numerical evaluation is provided by solving recent benchmark functions in IEEE CEC 2013.
群体优化的一种加速和减速策略
本文以鱼群避光所观察到的加速和减速机制为灵感,提出了一种基于鱼群智能的优化算法,以提高鱼群优化算法的精度和效率。通过与全局最优解的比较,每个粒子自适应地向最优解加速或减速。具体来说,在原有的粒子群优化算法(PSO)或其他粒子群优化算法的基础上增加了一个新的定向速度项。由于采用了SUSD机制,该算法与原粒子群和其他粒子群变体相比,精度和收敛速度都有很大提高。通过求解IEEE CEC 2013中最新的基准函数,给出了数值评价。
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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