An Improved Particle Swarm Optimization Algorithm and Its Application in Clustering analysis

doi:10.16182/j.issn1004731x.joss.19-0006

Abstract

Abstract: In this paper, a novel artificial fish swarm particle swarm optimization algorithm (AF-PSO) is proposed corresponding to the shortcomings of the standard particle swarm algorithm including the fast convergence speed in the initial stage, the easiness to fall into premature convergence in the late, the local optimization and the poor ability to global search. This paper firstly introduces the crowding factorδ and the Markov chain, and then adds the artificial fish swarm algorithm to the particle swarm optimization algorithm. By calculating the crowding factor, the velocity model is updated to switch among four modes: foraging, clustering, following and random. The simulation results show that the proposed AF-PSO algorithm has better performance compared with other improved PSO algorithms in synthesis. To further illustrate the application potential, the AF-PSO algorithm is successfully applied to the clustering analysis of oil pipeline leakage data. Experiment results demonstrate that the performance of the AF-PSO based K-means method is better than other clustering algorithms.

Key words: particle swarm optimization, artificial fish swarm algorithm, AF-PSO, K-means, Markov chain

CLC Number:

TP273

Wang Chuang, Zhang Yong, Li Xuegui, Dong Hongli. An Improved Particle Swarm Optimization Algorithm and Its Application in Clustering analysis[J]. Journal of System Simulation, 2020, 32(8): 1577-1587.

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