Combining max–min ant system with effective local search for solving the maximum set k-covering problem
作者：
Highlights：
• We propose a max–min ant system with memory ants and a novel local search for solving MKCP.
• The experimental comparisons with 7 best algorithms reveal that MMAS-ML outperforms other algorithms.
• Three key components of MMAS-ML are verified useful in the form of the experimental verification.
摘要
•We propose a max–min ant system with memory ants and a novel local search for solving MKCP.•The experimental comparisons with 7 best algorithms reveal that MMAS-ML outperforms other algorithms.•Three key components of MMAS-ML are verified useful in the form of the experimental verification.
论文关键词：Maximum set k-covering problem,Double layer selection heuristic,Enhanced configuration checking,Obvious row weighting,Max–min ant system
论文评审过程：Received 10 June 2021, Revised 14 December 2021, Accepted 17 December 2021, Available online 24 December 2021, Version of Record 5 January 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.108000
