期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 23, 期 4, 页码 556-571出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2018.2872453
关键词
Difficult-to-approximate (DtA); evolutionary algorithm; multiobjective optimization; Pareto front (PF) boundaries; test problem
资金
- Data Science and Artificial Intelligence Research Centre at Nanyang Technological University
- National Key Research and Development Program of China [2018YFC0809801]
- National Science Foundation of China [61573279]
- ANR/RGC Joint Research Scheme - Research Grants Council of the Hong Kong Special Administrative Region, China
- France National Research Agency Project [A-CityU101/16]
In some real-world applications, it has been found that the performance of multiobjective optimization evolutionary algorithms (MOEAs) may deteriorate when boundary solutions in the Pareto front (PF) are more difficult to approximate than others. Such a problem feature, referred to as difficult-to-approximate (DtA) PF boundaries, is seldom considered in existing multiobjective optimization test problems. To fill this gap and facilitate possible systematic studies, we introduce a new test problem generator. The proposed generator enables the design of test problems with controllable difficulties regarding the feature of DtA PF boundaries. Three representative MOEAs, NSGA-II, SMS-EMOA, and MOEA/D-DRA, are performed on a series of test problems created using the proposed generator. Experimental results indicate that all the three algorithms perform poorly on the new test problems. Meanwhile, a modified variant of MOEA/D-DRA, denoted as MOEA/D-DRA-UT, is validated to be more effective in dealing with these problems. Subsequently, it is concluded that the rational allocation of computational resources between different PF parts is crucial for MOEAs to handle the problems with DtA PF boundaries.
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