Move acceptance in local search metaheuristics for cross-domain search
作者：
Highlights：
• Classification of local search metaheuristics based on their move acceptance methods.
• A concise overview of local search metaheuristics in relevant classes.
• Cross-domain performance comparison of 8 local search metaheuristics from each class.
• Simulated annealing (SA) has the best performance over 45 instances from 9 domains.
• Parameters of SA needs re-tuning for each domain to achieve this performance.
摘要
•Classification of local search metaheuristics based on their move acceptance methods.•A concise overview of local search metaheuristics in relevant classes.•Cross-domain performance comparison of 8 local search metaheuristics from each class.•Simulated annealing (SA) has the best performance over 45 instances from 9 domains.•Parameters of SA needs re-tuning for each domain to achieve this performance.
论文关键词：Combinatorial optimization,Parameter control,Stochastic local search,Trajectory methods,Search algorithms
论文评审过程：Received 9 November 2017, Revised 6 May 2018, Accepted 7 May 2018, Available online 9 May 2018, Version of Record 28 May 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.05.006
