• 算法设计与分析 •

### 改进的离散型萤火虫优化算法求解柔性作业车间调度问题

1. (1.西华师范大学数学与信息学院，四川南充637009;2.西华师范大学计算方法与应用研究所，四川南充637009)
• 出版日期:2021-08-19 发布日期:2021-08-19
• 作者简介:郑捷(1996—),女,四川岳池人,硕士研究生,研究方向:智能算法,数学建模,E-mail: 2092885201@qq.com; 通信作者:潘大志(1974—),男,四川三台人,教授,博士,研究方向:智能计算,算法设计,E-mail: pdzzj@126.com。
• 基金资助:
国家自然科学基金资助项目(11871059); 四川省教育厅自然科学基金资助项目(18ZA0469); 西华师范大学英才科研基金资助项目(17YC385)

### Improved Discrete Firefly Optimization Algorithm to Solve Flexible Job Shop Scheduling Problem

1. (1. School of Mathematics and Information, China West Normal University, Nanchong 637009, China;
2. Institute of Computing Method and Application Software， China West Normal University, Nanchong 637009, China)
• Online:2021-08-19 Published:2021-08-19

Abstract: Aiming at the problem that when solving the flexible job shop scheduling problem (FJSP), the traditional swarm intelligence optimization algorithm has some disadvantages, such as insufficient optimization ability and easy to fall into local optimum, taking minimizing the maximum completion time as targets, firefly algorithm (FA) is applied to solve flexible job shop scheduling problem (FJSP), and an improved discrete firefly algorithm (DFA) is proposed. Firstly, the relationship between the FA continuous optimization problem and the FJSP discrete optimization problem is established through two-stage coding. Secondly, a population initialization method is designed to ensure the quality and diversity of initial solutions. Then, an improved discrete firefly optimization algorithm is proposed and a local search algorithm is introduced to enhance the global search ability and local search ability of the algorithm. Finally, the standard example is simulated and the validity of DFA algorithm for FJSP is verified. Through simulation comparison with genetic algorithm and particle swarm optimization algorithm, the superiority of DFA in solving FJSP is verified.