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

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

针对传统的群智能优化算法在求解柔性作业车间调度问题(FJSP)时,存在寻优能力不足且易陷入局部最优等缺点,本文以最小化最大完工时间为目标,将萤火虫算法(FA)用于求解柔性作业车间调度问题,提出一种改进的离散型萤火虫算法(DFA)。首先,通过两段式编码建立FA连续优化问题与FJSP离散优化问题之间的联系;其次,设计一种群初始化方法,以确保初始解的质量以及多样性;然后,提出改进离散型萤火虫优化算法并引入局部搜索算法,加强算法的全局搜索能力和局部搜索能力;最后,对标准算例进行仿真,验证DFA算法求解FJSP的有效性。通过与遗传算法和粒子群优化算法进行仿真对比,表明了DFA求解FJSP的优越性。

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.