Self-adaptive particle swarm optimization : a review and analysis of convergence
Loading...
Date
Authors
Harrison, Kyle Robert
Engelbrecht, Andries P.
Ombuki-Berman, Beatrice M.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
Particle swarm optimization (PSO) is a population-based, stochastic search algorithm inspired by the flocking behaviour of birds. The PSO algorithm has been shown to be rather sensitive to its control parameters, and thus, performance may be greatly improved by employing appropriately tuned parameters. However, parameter tuning is typically a time-intensive empirical process. Furthermore, a priori parameter tuning makes the implicit assumption that the optimal parameters of the PSO algorithm are not time-dependent. To address these issues, self-adaptive particle swarm optimization (SAPSO) algorithms adapt their control parameters throughout execution. While there is a wide variety of such SAPSO algorithms in the literature, their behaviours are not well understood. Specifically, it is unknown whether these SAPSO algorithms will even exhibit convergent behaviour. This paper addresses this lack of understanding by investigating the convergence behaviours of 18 SAPSO algorithms both analytically and empirically. This paper also empirically examines whether the adapted parameters reach a stable point and whether the final parameter values adhere to a well-known convergence criterion. The results depict a grim state for SAPSO algorithms; over half of the SAPSO algorithms exhibit divergent behaviour while many others prematurely converge.
Description
Keywords
Particle swarm optimization (PSO), Self-adaptive particle swarm optimization (SAPSO), Parameter control, Convergence, Algorithm, Stability analysis
Sustainable Development Goals
Citation
Harrison, K.R., Engelbrecht, A.P. & Ombuki-Berman, B.M. Self-adaptive particle swarm optimization: a review and analysis of convergence. Swarm Intelligence (2018) 12: 187-226. https://doi.org/10.1007/s11721-017-0150-9.