Abstract An improved non-dominated sorting genetic algorithm (INSGA) is introduced for multi-objective optimization. In order to keep the diversity of the population, a modified elite preservation strategy is adopted and the evaluation of solutions’ crowding degree is integrated in crossover operations during the evolution. The INSGA is compared with the NSGA-II and other algorithms by applications to five classical test functions and an environmental/economic dispatch (EED) problem in power systems. It is shown that the Pareto solution obtained by INSGA has a good convergence and diversity.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An Improved Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization Based on Crowding Distance


    Beteiligte:
    Xia, Tian-liang (Autor:in) / Zhang, Shao-hua (Autor:in)


    Erscheinungsdatum :

    01.01.2014


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Multi-objective Optimization of the PMS Based on Non-dominated Sorting Genetic Algorithm II

    Xu, Peijun / Chen, Yongfu / Zhang, Yunqing et al. | SAE Technical Papers | 2015


    Multi-objective Optimization of the PMS Based on Non-dominated Sorting Genetic Algorithm II

    Chen, Yongfu / Tang, Zhengfei / Xu, Peijun et al. | British Library Conference Proceedings | 2015