期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 24, 期 1, 页码 193-200出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2019.2909744
关键词
Evolutionary algorithms; multimodal multiobjective optimization; performance indicators; test problems
资金
- Program for Guangdong Introducing Innovative and Enterpreneurial Teams [2017ZT07X386]
- Shenzhen Peacock Plan [KQTD2016112514355531]
- Science and Technology Innovation Committee Foundation of Shenzhen [ZDSYS201703031748284]
- Program for University Key Laboratory of Guangdong Province [2017KSYS008]
- National Natural Science Foundation of China [61876075]
Multimodal multiobjective optimization aims to find all Pareto optimal solutions, including overlapping solutions in the objective space. Multimodal multiobjective optimization has been investigated in the evolutionary computation community since 2005. However, it is difficult to survey existing studies in this field because they have been independently conducted and do not explicitly use the term multimodal multiobjective optimization. To address this issue, this letter reviews the existing studies of evolutionary multimodal multiobjective optimization, including studies published under names that are different from multimodal multiobjective optimization. Our review also clarifies open issues in this research area.
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