关键词: 遗传算法, 粒子群优化算法, 单峰, 多峰, 性能对比

Abstract: Genetic algorithm (GA) and particle swarm optimization (PSO) have been broadly used in many fields, such as function optimization, production scheduling, machine learning and data mining, etc. This paper makes comprehensive and systematic comparisons of GA and PSO on dealing with a series of benchmark problems, analyzes their performance on solving unimodal and multimodal functions, and further tests the robustness of two algorithms for investigating the influences of parameters to the performance of the algorithms. This paper finally concludes the characteristics of the two algorithms and discusses their improvement strategies. The goal of this paper is to provide technical guidance for the selection of algorithms in engineering applications.

Key words: genetic algorithm, particle swarm optimization, unimodal, multimodal, performance comparison