粒子群优化收敛性分析综述

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Swarm Intelligence Research Pub Date : 2023-08-18 DOI:10.4018/ijsir.328092

Dereje Tarekegn, S. Tilahun, Tekle Gemechu

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引用次数: 0

摘要

粒子群优化（PSO）是一种流行的受自然启发的元启发式算法。它已被用于不同的应用。收敛性分析是粒子群算法的关键理论研究之一。本文讨论了粒子群算法在收敛性分析中的主要贡献。系统分类将用于审查目的。还强调了未来可能开展的工作，即通过理论视角和对所提出方法优点的实验结果的一般讨论，研究PSO变体处理COP的性能。

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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A Review on Convergence Analysis of Particle Swarm Optimization

Particle swarm optimization (PSO) is one of the popular nature-inspired metaheuristic algorithms. It has been used in different applications. The convergence analysis is among the key theoretical studies in PSO. This paper discusses major contributions in the convergence analysis of PSO. A systematic classification will be used for the review purpose. Possible future works are also highlighted as to investigate the performance of PSO variants to deal with COPs through theoretical perspective and general discussions on experimental results on merits of the proposed approach.

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来源期刊

International Journal of Swarm Intelligence Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

2.50

自引率

0.00%

发文量

期刊介绍： The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.