Multi-population and Self-adaptive Genetic Algorithm Based on Simulated Annealing for Permutation Flow Shop Scheduling Problem

摘要：

　　In order to solve the permutation flow shop scheduling problem,a multi-population and self-adaptive genetic algorithm based on simulated annealing is proposed in this paper.For the precocity problem of traditional genetic algorithm,the multi-population coevolution strategy is adopted.We introduce a squared term to improve traditional self-adaptive genetic operators,which can increase the searching efficiency and avoid getting into local optimum.A new cooling strategy is proposed to reinforce the ability of overall searching optimal solution.The algorithm is used to solve a series of typical Benchmark problems.Moreover,the results are compared with SGA,IGA,and GASA.The comparison demonstrates the effectiveness of the algorithm.

关键词： Permutation flow shop scheduling problem Multi-population Self-adaptive Simulated annealing Genetic algorithm

作者: Huimin Sun Jingwei Yu Hailong Wang

作者单位: School of Astronautics Institution,Harbin Institute of Technology,Aviation University of Air Force,Changchun,92 West Dazhi Street,Nan Gang District,Harbin 150001,China

会议类型: 国际会议

会议名称: The 2015 Chinese Intelligent Automation Conference(2015中国智能自动化会议)

会议地点: 福州

会议语种:英文

页码: 11-19

在线出版日期: 2015-05-08（万方平台首次上网日期，不代表论文的发表时间）