Parameter settings in particle swarm optimisation algorithms: a survey
Jing Li, Shi Cheng · International Journal of Automation and Control · 2022
In swarm intelligence, 'fair comparison' is critical for the performance evaluation of algorithms. In this paper, the setting of parameters in particle swarm optimisation (PSO) algorithms, which include the population size S, topology structure (number of neighbours k), inertia weight w, acceleration coefficient c1, c2, velocity constraint Vmax, and the boundary constraint strategy, are reviewed and analysed. Based on the analysis and discussion of parameters and the variants of PSO algorithms, a list of parameter settings of PSO algorithms and a recommendation of PSO comparison are given. To compare variants of PSO algorithms, a recommended solution maybe that all compared algorithms have the same number of population size and the maximum number of fitness evaluations, and the inertia weight w, acceleration coefficient c1, c2 are the same settings as its original version.