Performance Analysis and Parameter Selection of PSO Algorithms
WANG Dong-Feng;MENG Li;Department of Automation, North China Electric Power University;
Inertia weight and acceleration factors have significant impact on the performance of particle swarm optimization(PSO) algorithm. Through simulation experiments on twelve classical benchmark functions, this paper studies the algorithm s exploitation ability and optimization performance with different parameters. Based on the experimental results, we recommend a setting for fixed parameters. Furthermore, we study the situation where inertia weight remains unchanged and acceleration factors change with iterations. Then a setting for varying parameters is recommended. The recommended parameters setting methods are verified through 15 benchmark functions that were published in CEC2015.At the end of the paper, a discussion of the PSO application issue on continuous optimization problems and discrete optimization problems is given.
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