Coevolutionary Framework for Generalized Multimodal Multi-Objective Optimization-Reference-Cited by-同舟云学术

Coevolutionary Framework for Generalized Multimodal Multi-Objective Optimization

Published:2023-07 Issue:7 Volume:10 Page:1544-1556
ISSN:2329-9266
Container-title:IEEE/CAA Journal of Automatica Sinica
language:
Short-container-title:IEEE/CAA J. Autom. Sinica

Author:

Li Wenhua¹^ORCID,Yao Xingyi¹^ORCID,Li Kaiwen¹^ORCID,Wang Rui²^ORCID,Zhang Tao³^ORCID,Wang Ling⁴^ORCID

Affiliation:

1. College of Systems Engineering, National University of Defense Technology,Changsha,China,410073

2. College of Systems Engineering, National University of Defense Technology,Xiangjiang Laboratory,Changsha,China,410073

3. College of Systems Engineering, National University of Defense Technology,Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology,Changsha,China,410073

4. Tsinghua University,Department of Automation,Beijing,China,100084

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Information Systems,Control and Systems Engineering,Control and Optimization

Link

http://xplorestaging.ieee.org/ielx7/6570654/10153596/10153726.pdf?arnumber=10153726

Reference47 articles.

1. Using Neighborhood-Based Density Measures for Multimodal Multi-objective Optimization

2. Benefits and drawbacks for the use of epsilon-dominance in evolutionary multi-objective optimization

3. Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization

4. Combining Convergence and Diversity in Evolutionary Multiobjective Optimization

5. A two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective optimization

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