Opposition-based learning for competitive hub location: A bi-objective biogeography-based optimization algorithm
作者：
Highlights：
• A competitive hub location problem under three competition rules is modeled.
• A tuned multi-objective biogeography-based optimization is designed.
• The binary opposition-based learning is used to improve diversity.
• Opposition MOBBO and opposition NSGA-II are developed.
• A multi-objective coefficient of variation and TOPSIS are utilized to rank the algorithms.
摘要
•A competitive hub location problem under three competition rules is modeled.•A tuned multi-objective biogeography-based optimization is designed.•The binary opposition-based learning is used to improve diversity.•Opposition MOBBO and opposition NSGA-II are developed.•A multi-objective coefficient of variation and TOPSIS are utilized to rank the algorithms.
论文关键词：Competitive hub location,Evolutionary computations,Binary opposition-based learning,Multi-objective biogeography-based optimization,Non-dominated sorting genetic algorithm
论文评审过程：Received 18 October 2016, Revised 22 April 2017, Accepted 29 April 2017, Available online 1 May 2017, Version of Record 25 May 2017.
论文官网地址：https://doi.org/10.1016/j.knosys.2017.04.017
