Research on particle swarm optimization of variable parameter

Abstract

Aimed at particle swarm optimization, since there are a fewer adjustable parameters, when solving the multi-dimensional function, it is easy to meet premature convergence problem, so an improved particle swarm optimization of variable parameters is proposed. According to particle movement characteristics, the formula of particle velocity updating is improved to make all integrated into the corresponding weight factor; through weight factor, the particle optimization performance is adjusted. Three standard test functions are used for test, with comparison with other algorithms, and the simulation results show that by setting different weight factors, the proposed algorithm has better optimization precision and ability to execute, and the better result can be achieved when solving the multi-dimensional function.