A general framework for enhancing relaxed Pareto dominance methods in evolutionary many-objective optimization
Author:
Funder
Natural Science Foundation of China
Guangdong Provincial Key Laboratory
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11047-022-09889-z.pdf
Reference45 articles.
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4. Chen L, Deb K, Liu HL, Zhang Q (2021) Effect of objective normalization and penalty parameter on penalty boundary intersection decomposition-based evolutionary many-objective optimization algorithms. Evol Comput 29(1):157–186
5. Cheng R, Jin Y, Olhofer M, Sendhoff B (2016) A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 20(5):773–791
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