An Evolutionary Algorithm With Constraint Relaxation Strategy for Highly Constrained Multiobjective Optimization-Reference-Cited by-同舟云学术

An Evolutionary Algorithm With Constraint Relaxation Strategy for Highly Constrained Multiobjective Optimization

Published:2023-05 Issue:5 Volume:53 Page:3190-3204
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Sun Zhichao¹^ORCID,Ren Hang¹,Yen Gary G.²^ORCID,Chen Tianfu¹,Wu Junjie¹,An Hongyang¹,Yang Jianyu¹

Affiliation:

1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA

Funder

National Natural Science Foundation of China

Postdoctoral Innovation Talent Support Program

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/10106628/09733273.pdf?arnumber=9733273

Reference70 articles.

1. An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions

2. A comparative study of differential evolution variants for global optimization

3. Constraint-handling in nature-inspired numerical optimization: Past, present and future

4. A Dual-Population-Based Evolutionary Algorithm for Constrained Multiobjective Optimization

5. Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons

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