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《Computer Engineering and Applications》 2019-13
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Improved NSGA for Multi-Objective Flexible Job-Shop Scheduling Problem

JU Luyan;YANG Jianjun;ZHANG Jianbing;GUO Longlong;LI Suobin;College of Mechanical Engineering, Xi'an Shiyou University;School of Mechanical and Automotive Engineering, Qingdao University of Technology;  
During the evaluation process of the job-shop scheduling problem, the algorithm and multi-objective optimization are very important. Therefore, an improved genetic algorithm based on NSGA is proposed and the corresponding matrix coding, decoding and crossover operators are designed. To reduce the computational complexity and improve the performance of the algorithm, a novel non-dominated sorting method, adaptive mutation operators and elite retention strategies are introduced. The simulation experiments show that this non-dominated sorting method can get the Pareto optimal solutions quickly and correctly by dividing the whole population into three parts. This algorithm can make full use of the global searching ability of traditional genetic algorithm, prevent the occurrence of precocious phenomenon, and change the mutation probability according to the diversity of the population.
【CateGory Index】: TH165-4;TP18
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1 CHENG Zi'an;TONG Ying;SHEN Lijuan;YU Shuaishuai;LI Ming;College of Machinery and Transportation,Southwest Forestry University;;Improved bacteria foraging optimization algorithm for solving flexible job-shop scheduling problem[J];计算机应用;2015-S2
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