A novel preference-driven evolutionary algorithm for dynamic multi-objective problems-Reference-Cited by-同舟云学术

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A novel preference-driven evolutionary algorithm for dynamic multi-objective problems

Published:2024-08 Issue: Volume:89 Page:101638
ISSN:2210-6502
Container-title:Swarm and Evolutionary Computation
language:en
Short-container-title:Swarm and Evolutionary Computation

Author:

Wang Xueqing,Zheng Jinhua^ORCID,Hou Zhanglu,Liu Yuan,Zou Juan,Xia Yizhang,Yang Shengxiang^ORCID

Funder

Hunan Provincial Innovation Foundation for Postgraduate

Education Department of Hunan Province

Innovative Research Group Project of the National Natural Science Foundation of China

National Natural Science Foundation of China

Hunan Provincial Natural Science Foundation

Publisher

Elsevier BV

Reference56 articles.

1. Dynamic multiobjective optimization problems: Test cases, approximations, and applications;Farina;IEEE Trans. Evol. Comput.,2004

2. A multi-objective optimization framework for energy and asset management in an industrial microgrid;Choobineh;J. Clean. Prod.,2016

3. A genetic algorithm-based, hybrid machine learning approach to model selection;Bies;J. Pharmacokinet. Pharmacodynam.,2006

4. Key challenges and future directions of dynamic multi-objective optimisation;Helbig,2016

5. Multi-objective scheduling of dynamic job shop using variable neighborhood search;Adibi;Expert Syst. Appl.,2010

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