On the cooperation of meta-heuristics for solving many-objective problems: An empirical analysis including benchmark and real-world problems
作者：
Highlights：
• We analyze the cooperative hyper-heuristics for many-objective optimization.
• It uses multi-objective evolutionary algorithms (MOEAs) as low-level heuristics.
• Each MOEA has its own internal population, and exchange information.
• The migration procedure is capable of improving stagnated MOEAs.
• We compare the results to a state-of-the-art hyper-heuristic with favorable results.
摘要
•We analyze the cooperative hyper-heuristics for many-objective optimization.•It uses multi-objective evolutionary algorithms (MOEAs) as low-level heuristics.•Each MOEA has its own internal population, and exchange information.•The migration procedure is capable of improving stagnated MOEAs.•We compare the results to a state-of-the-art hyper-heuristic with favorable results.
论文关键词：Hyper-heuristic,Continuous optimization,Heuristic selection,Evolutionary algorithm,Many-objective optimization
论文评审过程：Received 2 January 2021, Revised 29 September 2021, Accepted 27 November 2021, Available online 20 December 2021, Version of Record 28 December 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116343
