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GEPSO: A new generalized particle swarm optimization algorithm
Mathematics and Computers in Simulation ( IF 4.6 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.matcom.2020.08.013
Davoud Sedighizadeh , Ellips Masehian , Mostafa Sedighizadeh , Hossein Akbaripour

Abstract Particle Swarm Optimization (PSO) algorithm is a nature-inspired meta-heuristic that has been utilized as a powerful optimization tool in a wide range of applications since its inception in 1995. Due to the flexibility of its parameters and concepts, PSO has appeared in many variants, probably more than any other meta-heuristic algorithm. This paper introduces the Generalized Particle Swarm Optimization (GEPSO) algorithm as a new version of the PSO algorithm for continuous space optimization, which enriches the original PSO by incorporating two new terms into the velocity updating equation. These terms aim to deepen the interrelations of particles and their knowledge sharing, increase variety in the swarm, and provide a better search in unexplored areas of the search space. Moreover, a novel procedure is utilized for dynamic updating of the particles’ inertia weights, which controls the convergence of the swarm towards a solution. Also, since parameters of heuristic and meta-heuristic algorithms have a significant influence on their performance, a comprehensive guideline for parameter tuning of the GEPSO is developed. The computational results of solving numerous well-known benchmark functions by the GEPSO, original PSO, Repulsive PSO (REPSO), PSO with Passive Congregation (PSOPC), Negative PSO (NPSO), Deterministic PSO (DPSO), and Line Search-Based Derivative-Free PSO (LS-DF-PSO) approaches showed that the GEPSO outperformed the compared methods in terms of mean and standard deviation of fitness function values and runtimes.

中文翻译:

GEPSO:一种新的广义粒子群优化算法

摘要 粒子群优化 (PSO) 算法是一种受自然启发的元启发式算法,自 1995 年问世以来一直作为强大的优化工具在广泛的应用中使用。由于其参数和概念的灵活性,PSO 出现了在许多变体中,可能比任何其他元启发式算法都多。本文介绍了广义粒子群优化(Generalized Particle Swarm Optimization,GEPSO)算法作为连续空间优化的PSO算法的新版本,通过在速度更新方程中加入两个新项,丰富了原有的PSO。这些术语旨在加深粒子及其知识共享的相互关系,增加群体中的多样性,并在搜索空间的未探索区域提供更好的搜索。而且,一种新颖的程序用于动态更新粒子的惯性权重,该程序控制群向解决方案的收敛。此外,由于启发式和元启发式算法的参数对其性能有显着影响,因此开发了 GEPSO 参数调整的综合指南。GEPSO、原始粒子群算法、排斥粒子群算法(REPSO)、被动聚合粒子群算法(PSOPC)、负粒子群算法(NPSO)、确定性粒子群算法(DPSO)和基于线搜索的导数求解众多知名基准函数的计算结果-Free PSO (LS-DF-PSO) 方法表明 GEPSO 在适应度函数值和运行时间的均值和标准差方面优于比较方法。由于启发式和元启发式算法的参数对其性能有显着影响,因此开发了 GEPSO 参数调整的综合指南。GEPSO、原始粒子群算法、排斥粒子群算法(REPSO)、被动聚合粒子群算法(PSOPC)、负粒子群算法(NPSO)、确定性粒子群算法(DPSO)和基于线搜索的导数求解众多知名基准函数的计算结果-Free PSO (LS-DF-PSO) 方法表明 GEPSO 在适应度函数值和运行时间的均值和标准差方面优于比较方法。由于启发式和元启发式算法的参数对其性能有显着影响,因此开发了 GEPSO 参数调整的综合指南。GEPSO、原始粒子群算法、排斥粒子群算法(REPSO)、被动聚合粒子群算法(PSOPC)、负粒子群算法(NPSO)、确定性粒子群算法(DPSO)和基于线搜索的导数求解众多知名基准函数的计算结果-Free PSO (LS-DF-PSO) 方法表明 GEPSO 在适应度函数值和运行时间的均值和标准差方面优于比较方法。
更新日期:2021-01-01
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