Abstract
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. 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.
Highlights
Artificial intelligence (AI) is the intelligence exhibited by machines
We provided advances with particle swarm optimization (PSO), including its modifications, population topology, hybridization, extensions, theoretical analysis, and parallel implementation
When the behavior of particle swarm explained below is reexamined, it is clear that the four principles of self-organization defined by Bonabeau et al [14] are fully satisfied
Summary
Artificial intelligence (AI) is the intelligence exhibited by machines. It is defined as “the study and design of intelligent agents” [1], where an intelligent agent represents a system that perceives its environment and takes action that maximizes its success chance. It is a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which traditional approaches are ineffective or infeasible. Swarm intelligence (SI) is a part of EC It researches the collective behavior of decentralized, self-organized systems, natural or artificial. Kennedy and Eberhart [6] proposed a particle swarm optimization (PSO) method based on bird flocking. Those are two most famous SI-based optimization algorithms. Krishnanand and Ghose [11] proposed glowworm swarm optimization (GSO) method, the agents in which are thought of as glowworms that carry a luminescence quantity called luciferin along with them. IEEE Explorer and Google Scholar were used
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