A novel quantum-behaved particle swarm optimization (QPSO) algorithm, the dual sub-swarm interaction QPSO algorithm based on different correlation coefficients (DCC-QPSO), is proposed by constructing master-slave sub-swarms with different potential well centres. In the novel algorithm, the master sub-swarm and the slave sub-swarm have different functinons during the evolutionary process through separate information processing strategies. The master subswarm is conducive to maintaining population diversity and enhancing the global search ability of particles. The slave sub-swarm accelerates the convergence rate and strengthens the particles’ local searching ability. With the critical information contained in the search space and results of the basic QPSO algorithm, this new algorithm avoids the rapid disappearance of swarm diversity and enhances searching ability through collaboration between sub-swarms. Experimental results on six test functions show that DCC-QPSO outperforms the traditional QPSO algorithm regarding optimization of multimodal functions, with enhancement in both convergence speed and precision.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Dual sub-swarm interaction QPSO algorithm based on different correlation coefficients


    Beteiligte:
    Wu, Tao (Autor:in) / Xie, Lei (Autor:in) / Chen, Xi (Autor:in) / He, Jia (Autor:in)

    Erscheinungsdatum :

    18.06.2018


    Anmerkungen:

    Automatika : časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije ; ISSN 0005-1144 (Print) ; ISSN 1848-3380 (Online) ; Volume 58 ; Issue 4


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Electric vehicle charging strategy optimization method based on QPSO algorithm

    LEI XUETING / XU MINGYU / CHEN XIAOGUANG et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Modeling method of variable cycle engine based on QPSO hybrid algorithm

    Xiao, Hongliang / Li, Huacong / Li, Jia et al. | British Library Online Contents | 2018


    The optimal thrust allocation based on QPSO algorithm for dynamic positioning vessels

    Ji, M. / Yi, B. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2014



    A Multi-Vehicle Tracking Algorithm Based on QPSO Clustering Algorithm and Particle Position Matching Template

    Liu, Shao Jiang ;Mo, Wei Jian ;Wan, Zhi Ping | Trans Tech Publications | 2013