A diversity preservation method for expensive multi-objective combinatorial optimization problems using Novel-First Tabu Search and MOEA/D
作者：
Highlights：
• A greedy strategy that uses knowledge-assisted local search methods is developed.
• The greedy strategy is combined with the MOEA/D algorithm.
• The method is evaluated on five well-known multi-objective combinatorial problems.
• The method is evaluated on the real-world problem of Well Placement Optimization.
• It achieves faster convergence in comparison with state-of-the-art algorithms.
摘要
•A greedy strategy that uses knowledge-assisted local search methods is developed.•The greedy strategy is combined with the MOEA/D algorithm.•The method is evaluated on five well-known multi-objective combinatorial problems.•The method is evaluated on the real-world problem of Well Placement Optimization.•It achieves faster convergence in comparison with state-of-the-art algorithms.
论文关键词：Expensive multi-objective combinatorial optimization,Decomposition-based methods,Diversity preservation,Black-box optimization,Novel-First Tabu Search
论文评审过程：Received 21 October 2021, Revised 22 March 2022, Accepted 12 April 2022, Available online 19 April 2022, Version of Record 27 April 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117251
