Reactive Search strategies using Reinforcement Learning, local search algorithms and Variable Neighborhood Search

作者：

Highlights：

• The proposed algorithm has a stable behavior with standard deviation lesser than 0.5%.

• Two instances from the TSPLIB are 100% optimal, 3 are over 99%, 4 are over 97%.

• The proposed method has superior performance in all instances to the values of O.F.

• The performance of processing time of the proposed algorithm is superior to all other.

摘要

•The proposed algorithm has a stable behavior with standard deviation lesser than 0.5%.•Two instances from the TSPLIB are 100% optimal, 3 are over 99%, 4 are over 97%.•The proposed method has superior performance in all instances to the values of O.F.•The performance of processing time of the proposed algorithm is superior to all other.

论文关键词：Reactive Search,Reinforcement Learning,Local search,Variable Neighborhood Search,Combinatorial optimization

论文评审过程：Available online 14 February 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.01.040