改进离散型飞蛾扑火优化算法求解柔性作业车间调度问题

Improved Discrete Moth-flame Optimization Algorithm for Solving the Flexible Job Shop Scheduling Problem

针对以最小化最大完工时间为目标的柔性作业车间调度问题(FJSP),提出一种改进离散型飞蛾扑火优化算法(IDMFO)。首先,基于集成法的求解思想设计两段式编码转化机制,建立染色体连续空间与问题离散决策空间的映射关系;其次,采用有效的种群初始化方法,保证种群的多样性和质量,加快算法收敛速度;然后,为了进一步提高算法的全局搜索能力,设计新颖的随机更新算子和基于Levy飞行轨迹的随机游走策略;最后通过标准算例测试及与其他算法求解结果的对比,验证了IDMFO算法在求解FJSP问题方面的有效性和优越性。

For the flexible job shop scheduling problem(FJSP)with the goal of minimizing the maximum completion time,an improved moth-flame optimization algorithm(IDMFO)is proposed.Based on the solution idea of integration,the two-segment coding transformation mechanism is designed to establish the mapping relationship between chromosome continuous space and the problem discrete decision-making space.The effective population initialization method is used to ensure the diversity and quality of the population and accelerate the convergence speed of the algorithm.In order to further improve the global search ability of the algorithm,a novel random update operator and random walk strategy are designed based on Levy's flight trajectory.Finally,the validity and superiority of IDMFO algorithm in solving FJSP problems are verified by standard study test and comparison with other algorithm results.