A comprehensive survey on particle swarm optimization algorithm and its applications
Summary: Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by \textit{J. Kennedy} and \textit{R. Eberhart} [``Particle swarm optimization'', in: Proceedings of the international conference on neural networks, ICNN'95, Perth, WA, Australia, 27 Nov.--1 Dec. 1995. Los Alamitos, CA: IEEE Computer Society. 1942--1948 (1995; \url{doi:10.1109/ICNN.1995.488968})]. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.
scientific article; zbMATH DE number 6927600
scientific article