On Stability Analysis of Particle Swarm Optimization Algorithm

Anshul Gopal; Mohammad Mahdi Sultani; Jagdish Chand Bansal

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On Stability Analysis of Particle Swarm Optimization Algorithm

机译：粒子群优化算法的稳定性分析

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摘要

Particle swarm optimization (PSO) is one of the most efficient and popular swarm intelligence-based search algorithmsfor continuous optimization. PSO provides the solutions probabilistically. Therefore, finding error bound during the searchprocess can help in developing a better PSO. Stability analysis of an algorithm provides the information about error bounds.Stability analysis of PSO with inertia weight and constriction coefficient is carried out by von Neumann stability criterion.Conditions on acceleration parameters, constriction coefficient and inertia weight are obtained for stability.

机译：粒子群优化（PSO）是最有效和流行的基于群体智能的连续优化搜索算法之一。 PSO可能提供解决方案。因此，在搜索过程中查找错误界限可以帮助开发更好的PSO。算法的稳定性分析提供了有关误差范围的信息。根据冯·诺依曼稳定性准则对具有惯性权重和压缩系数的PSO进行稳定性分析，获得了加速度参数，压缩系数和惯性权重的条件以达到稳定性。

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来源
《Arabian Journal for Science and Engineering》 |2020年第4期|2385-2394|共10页
作者
Anshul Gopal; Mohammad Mahdi Sultani; Jagdish Chand Bansal;
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作者单位

South Asian University New Delhi India;

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收录信息美国《科学引文索引》(SCI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Particle swarm optimization (PSO) algorithm; Stability analysis; Finite difference scheme; von Neumann stability criterion; Inertia weight; Constriction coefficient;

机译：粒子群优化（PSO）算法;稳定性分析;有限差分方案;冯·诺依曼稳定性准则;惯性重量收缩系数;

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On Stability Analysis of Particle Swarm Optimization Algorithm

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