Particle Swarm Optimization or Differential Evolution—A comparison

差异进化 多群优化 元启发式 粒子群优化 计算机科学 元优化 并行元启发式 帝国主义竞争算法 数学优化 群体行为 最优化问题 进化计算 算法 人工智能 数学
作者
A. Piotrowski,Jarosław J. Napiórkowski,Agnieszka E. Piotrowska
出处
期刊:Engineering Applications of Artificial Intelligence [Elsevier BV]
卷期号:121: 106008-106008 被引量:58
标识
DOI:10.1016/j.engappai.2023.106008
摘要

In the mid 1990s two landmark metaheuristics have been proposed: Particle Swarm Optimization and Differential Evolution. Their initial versions were very simple, but rapidly attracted wide attention. During the last quarter century hundreds of variants of both optimization algorithms have been proposed and applied in almost any field of science or engineering. However, no broader comparison of performance between both families of methods has been presented so far. In the present paper ten Particle Swarm Optimization and ten Differential Evolution variants, from historical ones from the 1990s up to the most recent ones from 2022, are compared on numerous single-objective numerical benchmarks and 22 real-world problems. On average Differential Evolution algorithms clearly outperform Particle Swarm Optimization ones. Such advantage of Differential Evolution over Particle Swarm Optimization is in contradiction with popularity: In the literature Particle Swarm Optimization algorithms are two–three times more frequently used than Differential Evolution ones. Problems for which Particle Swarm Optimization performs better than Differential Evolution do exist but are relatively few. Although this result may be an effect of the choice of specific variants, experimental settings or problems used for comparison, some re-consideration of algorithmic philosophy may be needed for Particle Swarm Optimization variants to make them more competitive.
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