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Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses

International Journal of Automation and computing 2021年第2期18卷 155-169页

作者： Wen-Jing Hong Peng Yang Ke Tang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Department of Management Science University of Science and Technology of ChinaHefei 230027China Guangdong–Hong Kong–Macao Greater Bay Area Center for Brain Science and Brain–inspired Intelligence Guangzhou 510515China

Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world *** evolutionary algorithms(EAs)have been widely acknowledged as a mainstream method for MOPs,most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision *** recently,it has been reported that traditional multi-objective EAs(MOEAs)suffer severe deterioration with the increase of decision *** a result,and motivated by the emergence of real-world large-scale MOPs,investigation of MOEAs in this aspect has attracted much more attention in the past *** paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two *** the key difficulties of the large-scale MOPs,the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision *** the perspective of methodology,the large-scale MOEAs are categorized into three classes and introduced respectively:divide and conquer based,dimensionality reduction based and enhanced search-based *** future research directions are also discussed.

关键词： Large-scale multi-objective optimization high-dimensional search space evolutionary computation evolutionary algorithms scalability

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