A self-guided differential evolution with neighborhood search for permutation flow shop scheduling
作者：
Highlights：
• Constructive heuristics and discrete harmony search are used to initialize.
• A guided agent based on the probabilistic model is proposed.
• Multiple mutation and crossover based on the guided agent are proposed.
• Neighborhood search based on the variable neighborhood search is designed.
• The convergence of the proposed algorithm is analyzed with Markov chain.
摘要
•Constructive heuristics and discrete harmony search are used to initialize.•A guided agent based on the probabilistic model is proposed.•Multiple mutation and crossover based on the guided agent are proposed.•Neighborhood search based on the variable neighborhood search is designed.•The convergence of the proposed algorithm is analyzed with Markov chain.
论文关键词：Permutation flow shop scheduling,Guided individual,Differential evolution,Markov chain,Variable neighborhood search
论文评审过程：Received 23 September 2015, Revised 3 November 2015, Accepted 3 December 2015, Available online 11 December 2015, Version of Record 23 January 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.12.001
