Influence of Inertia Weight and Learning Factor on Performance of Standard PSO Algorithm

doi:10.13438/j.cnki.jdzk.2019.04.005

Abstract

Abstract:

Two types of parameters, inertia weight and learning factor, are the main factors affecting the accuracy and convergence speed of the standard PSO algorithm. Based on the standard PSO algorithm, the influence of different value-taking strategies of two types of parameters on the optimization results of commonly used test functions is analyzed to explore its impact on the performance of the standard PSO algorithm. The experimental results show that the proper dynamic change of the two types of parameters can significantly improve the optimization accuracy and convergence speed of the unimodal function, and can improve the optimization probability of the bimodal and multimodal functions. The inertia weight mainly affects the convergence speed of the algorithm. As the inertia weight increases, the convergence speed of the algorithm increases. The learning factor mainly affects the optimization precision of the algorithm. When the two learning factors increase and decrease, the precision of the optimization increases. The inertia weight increases with the two learning factors. The increase and decrease of the change, or the decrease of the inertia weight combined with the increase and decrease of the two learning factors, can significantly improve the performance of the standard PSO algorithm.

Key words: standard particle swarm optimization, inertia weight, learning factors, convergence speed, optimization accuracy

SONG Mongpei, MO Liping, ZHOU Kaiqing. Influence of Inertia Weight and Learning Factor on Performance of Standard PSO Algorithm[J]. Journal of Jishou University（Natural Sciences Edition), DOI: 10.13438/j.cnki.jdzk.2019.04.005.

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