Enhancing the Convergence Ability of Evolutionary Multi-objective Optimization Algorithms with Momentum-Reference-Cited by-同舟云学术

Enhancing the Convergence Ability of Evolutionary Multi-objective Optimization Algorithms with Momentum

Published:2024-07-14 Issue: Volume:9 Page:476-483
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Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Chen Longcan¹²^ORCID,Pang Lie Meng¹^ORCID,Zhang Qingfu²^ORCID,Ishibuchi Hisao¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China

2. Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong, Hong Kong

Funder

National Natural Science Foundation of China

Publisher

ACM

Reference21 articles.

1. SMS-EMOA: Multiobjective selection based on dominated hypervolume

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3. Longcan Chen, Lie Meng Pang, and Hisao Ishibuchi. 2022. New solution creation operator in MOEA/D for faster convergence. In Parallel Problem Solving from Nature - PPSN XVII (Lecture Notes in Computer Science), Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tušar (Eds.). Springer International Publishing, Cham, 234--246.

4. Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

5. A benchmark test suite for evolutionary many-objective optimization