Evaluation of Many-Objective Evolutionary Algorithms by Hesitant Fuzzy Linguistic Term Set and Majority Operator
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Over the past few decades, many-objective evolutionary algorithms have been proposed and presented as competitive compared with state-of-the-art algorithms. The evaluation of these algorithms involves many performance metrics, considered as a multiple criteria decision-making problem. In order to fairly and faithfully evaluate these algorithms, a novel evaluation approach based on hesitant fuzzy linguistic term set and majority operator is proposed. Hesitant fuzzy linguistic term set is used to express the opinions of experts, and majority operator is used to aggregate the opinions of experts. The framework for evaluation is presented, in which comprehensive performance metrics are proposed. An experimental study is designed to validate the proposed method. The experimental results indicate that the proposed approach is accurate and effective; the ability of algorithms to solve many-objective problems relies on both algorithms and the features of problems. Finally, the proposed method is used to evaluate the meteorological disaster that occurred in China in 2008.
KeywordsMany-objective evolutionary algorithm Hesitant fuzzy linguistic term set Majority Evaluation Meteorological disaster
This study was funded by China Natural Science Foundation (Grant Nos. 71503134, 91546117, 71373131), Key Project of National Social and Scientific Fund Program (16ZDA047) and philosophy and Social Sciences in Universities of Jiangsu (Grant No. 2016SJB630016).
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