计算机工程与应用2019，Vol.55Issue(13)：260-265,270,7.DOI:10.3778/j.issn.1002-8331.1809-0246
改进NSGA算法求解多目标柔性车间作业调度问题
Improved NSGA for Multi-Objective Flexible Job-Shop Scheduling Problem
JU Luyan 1YANG Jianjun 2ZHANG Jianbing 1GUO Longlong 1LI Suobin1
作者信息
- 1. College of Mechanical Engineering, Xi’an Shiyou University, Xi’an 710065, China 2.School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, Shandong 266520, China
- 2. School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, Shandong 266520, China
- 折叠
摘要
Abstract
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 perfor-mance 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.关键词
柔性车间作业调度/多目标优化/非劣前沿分级遗传算法Key words
flexible job-shop scheduling problem/multi-objective optimization/non-dominated sorting genetic algorithm分类
信息技术与安全科学引用本文复制引用
JU Luyan,YANG Jianjun,ZHANG Jianbing,GUO Longlong,LI Suobin..改进NSGA算法求解多目标柔性车间作业调度问题[J].计算机工程与应用,2019,55(13):260-265,270,7.
