Sectorization problems have significant challenges arising from the many objectives that must be optimised simultaneously. Several methods exist to deal with these many-objective optimisation problems, but each has its limitations. This paper analyses an application of Preference Inspired Co-Evolutionary Algorithms, with goal vectors (PICEA-g) to sectorization problems. The method is tested on instances of different size difficulty levels and various configurations for mutation rate and population number. The main purpose is to find the best configuration for PICEA-g to solve sectorization problems. Performance metrics are used to evaluate these configurations regarding the solutions’ spread, convergence, and diversity in the solution space. Several test trials showed that big and medium-sized instances perform better with low mutation rates and large population sizes. The opposite is valid for the small size instances.


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    Titel :

    An Application of Preference-Inspired Co-Evolutionary Algorithm to Sectorization


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Machado, José (Herausgeber:in) / Soares, Filomena (Herausgeber:in) / Trojanowska, Justyna (Herausgeber:in) / Yildirim, Sahin (Herausgeber:in) / Vojtěšek, Jiří (Herausgeber:in) / Rea, Pierluigi (Herausgeber:in) / Gramescu, Bogdan (Herausgeber:in) / Hrybiuk, Olena O. (Herausgeber:in) / Öztürk, Elif (Autor:in) / Rocha, Pedro (Autor:in)

    Kongress:

    International Conference Innovation in Engineering ; 2022 ; Minho, Portugal June 28, 2022 - June 30, 2022



    Erscheinungsdatum :

    21.06.2022


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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