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A theoretical guideline for designing an effective adaptive particle swarm
IEEE Transactions on Evolutionary Computation ( IF 14.3 ) Pub Date : 2020-02-01 , DOI: 10.1109/tevc.2019.2906894
Mohammad Reza Bonyadi

In this paper, the underlying assumptions that have been used for designing adaptive particle swarm optimization (PSO) algorithms in the past years are theoretically investigated. I relate these assumptions to the movement patterns of particles controlled by coefficient values (inertia weight and acceleration coefficients) and introduce three factors, namely the autocorrelation of the particle positions, the average movement distance of the particle in each iteration, and the focus of the search, that describe these movement patterns. I show how these factors represent movement patterns of a particle within a swarm and how they are affected by particle coefficients (i.e., inertia weight and acceleration coefficients). I derive equations that provide exact coefficient values to guarantee to achieve the desired movement pattern defined by these three factors within a swarm. I then relate these movements to the searching capability of particles and provide a guideline for designing potentially successful adaptive methods to control coefficients in particle swarm. Finally, I propose a new simple time adaptive particle swarm and compare its results with previous adaptive particle swarm approaches. Experiments show that the theoretical findings indeed provide a beneficial guideline for the successful adaptation of the coefficients in the PSO algorithm.

中文翻译:

设计有效自适应粒子群的理论指南

在本文中,从理论上研究了过去几年用于设计自适应粒子群优化 (PSO) 算法的基本假设。我将这些假设与由系数值(惯性权重和加速度系数)控制的粒子的运动模式联系起来,并引入了三个因素,即粒子位置的自相关性、粒子在每次迭代中的平均运动距离以及粒子的焦点搜索,描述这些运动模式。我展示了这些因素如何代表群体内粒子的运动模式以及它们如何受粒子系数(即惯性权重和加速度系数)的影响。我推导出提供精确系数值的方程,以保证在群体内实现由这三个因素定义的所需运动模式。然后,我将这些运动与粒子的搜索能力联系起来,并为设计可能成功的自适应方法来控制粒子群中的系数提供指导。最后,我提出了一种新的简单时间自适应粒子群,并将其结果与以前的自适应粒子群方法进行比较。实验表明,理论发现确实为 PSO 算法中系数的成功适应提供了有益的指导。最后,我提出了一种新的简单时间自适应粒子群,并将其结果与以前的自适应粒子群方法进行比较。实验表明,理论发现确实为 PSO 算法中系数的成功适应提供了有益的指导。最后,我提出了一种新的简单时间自适应粒子群,并将其结果与以前的自适应粒子群方法进行比较。实验表明,理论发现确实为 PSO 算法中系数的成功适应提供了有益的指导。
更新日期:2020-02-01
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