Abstract
Particle swarm optimization (PSO) is a population-based stochastic optimization originating from artificial life and evolutionary computation. PSO is motivated by the social behavior of organisms, such as bird flocking, fish schooling and human social relations. Its properties of low constraint on the continuity of objective function and ability of adapting to the dynamic environment make PSO become one of the most important swarm intelligence algorithms. However, compared to the various version of modified PSO and the corresponding applications in many domains, there has been very little research on the PSO’s convergence analysis. So the current paper, to begin with, elaborates the basic principles of standard PSO. Then the existing work on the convergence analyses of PSO in the literatures is thoroughly surveyed, which plays an important role in establishing the solid theoretical foundation for PSO algorithm. In the end, some important conclusions and possible research directions of PSO that need to be studied in the future are proposed.
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