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An analysis of parameter sensitivities of preference-inspired co-evolutionary algorithms

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  • Rui Wang
  • Maszatul M. Mansor
  • Robin C. Purshouse
  • Peter J. Fleming

Abstract

Many-objective optimisation problems remain challenging for many state-of-the-art multi-objective evolutionary algorithms. Preference-inspired co-evolutionary algorithms (PICEAs) which co-evolve the usual population of candidate solutions with a family of decision-maker preferences during the search have been demonstrated to be effective on such problems. However, it is unknown whether PICEAs are robust with respect to the parameter settings. This study aims to address this question. First, a global sensitivity analysis method – the Sobol’ variance decomposition method – is employed to determine the relative importance of the parameters controlling the performance of PICEAs. Experimental results show that the performance of PICEAs is controlled for the most part by the number of function evaluations. Next, we investigate the effect of key parameters identified from the Sobol’ test and the genetic operators employed in PICEAs. Experimental results show improved performance of the PICEAs as more preferences are co-evolved. Additionally, some suggestions for genetic operator settings are provided for non-expert users.

Suggested Citation

  • Rui Wang & Maszatul M. Mansor & Robin C. Purshouse & Peter J. Fleming, 2015. "An analysis of parameter sensitivities of preference-inspired co-evolutionary algorithms," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(13), pages 2407-2420, October.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:13:p:2407-2420
    DOI: 10.1080/00207721.2015.1008600
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    Cited by:

    1. Wang, Rui & Li, Guozheng & Ming, Mengjun & Wu, Guohua & Wang, Ling, 2017. "An efficient multi-objective model and algorithm for sizing a stand-alone hybrid renewable energy system," Energy, Elsevier, vol. 141(C), pages 2288-2299.
    2. Wang, Rui & Xiong, Jian & He, Min-fan & Gao, Liang & Wang, Ling, 2020. "Multi-objective optimal design of hybrid renewable energy system under multiple scenarios," Renewable Energy, Elsevier, vol. 151(C), pages 226-237.

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