Abstract The optimization of a complex system with multiple subsystems is a tough problem. In this paper, a Decentralized differential evolutionary algorithm (DDEA) is proposed. The simulations for both DDEA and centralized DE on three benchmark functions are carried out. The numerical results show that DDEA is efficient to solve decentralized optimization problems. On these problems, the proposed DDEA outperforms centralized DE in convergence.


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    Titel :

    Decentralized Differential Evolutionary Algorithm for Large-Scale Networked Systems


    Beteiligte:
    Han, Guanghong (Autor:in) / Chen, Xi (Autor:in) / Zhao, Qianchuan (Autor:in)


    Erscheinungsdatum :

    01.01.2019


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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