Abstract
Particle swarm optimization (PSO) explores global optimal solution through exploiting the particle's memory and the swarm's memory. Its properties of low constraint on the continuity of objective function and joint of search space, and ability of adapting to dynamic environment make PSO become one of the most important swarm intelligence methods and evolutionary computation algorithms. The fundamental and standard algorithm is introduced firstly. Then the work on the algorithm improvement during the past years is surveyed, as well as the applications on the multi-objective optimization, neural networks and electronics, etc. Finally, the problems remaining unresolved and some directions of PSO research are discussed.
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