Particle swarm optimization research base on quantum self-learning behavior

Article type: Research Article

Authors: Liao, Yifan^* | Qin, Guojun | Liu, Fu

Affiliations: School of Information and Electromechanical Engineering, Hunan International Economics University, Changsha, Hunan, China

Correspondence: [*] Corresponding author: Yifan Liao, School of Information and Electromechanical Engineering, Hunan International Economics University, Changsha, Hunan 410205, China. E-mail: [email protected].

Abstract: The quantum behavior particle swarm optimization algorithm is analyzed in this paper. The swarm particle search behavior is studied in the algorithm. The local attractive points of the algorithm are analyzed. The different search environments are given for particles in the search process. The algorithm can adaptively learn to optimize the problem environment, and appropriate learning mode is adopt to improve the overall optimization performance of the algorithm. The self-learning quantum particle swarm optimization algorithm is compared with other improved methods by CEC2014 benchmark test function. Finally, the results are analyzed. The simulation results show that the self-learning method can significantly improve the performance of the quantum particle swarm optimization algorithm.

Keywords: Particle swarm optimization (PSO), quantum behavior, self-learning, local attract point, searching model

DOI: 10.3233/JCM-193644

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 20, no. 1, pp. 91-99, 2020