A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

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

A Improved Particle Swarm Optimization Algorithm with Dynamic Acceleration Coefficients

Particle swarm optimization (PSO) is one of the famous heuristic methods. However, this method may suffer to trap at local minima especially for multimodal problem. This paper proposes a modified particle swarm optimization with dynamic acceleration coefficients (ACPSO). To efficiently control the local search and convergence to the global optimum solution, dynamic acceleration coefficients are introduced to PSO. To improve the solution quality and robustness of PSO algorithm, a new best mutation method is proposed to enhance the diversity of particle swarm and avoid premature convergence. The effectiveness of ACPSO algorithm is tested on different benchmarks. Simulation results found that the proposed ACPSO algorithm has good solution quality and more robust than other methods reported in previous work