基于混合果蝇-遗传算法求解柔性作业车间调度问题

A Hybrid Algorithm of Fruit Fly Optimization Algorithm and Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem

摘要:

根据柔性作业车间调度问题的特点,针对不同生产效率的并行设备,以完工时间最小化为目标建立优化模型,提出了混合果蝇优化算法和遗传算法的两阶段组合算法(FOA-GA). 在嗅觉阶段,通过局部路径搜索技术进行生产路径寻优；在视觉阶段,结合遗传算法的交叉和竞争机制,进行个体间的信息交换,利用寻优变异算子和常规变异算子进行两部分变异,再引入自适应动态转移算子进行调整以加快收敛速度. 在生产实例中,将FOA-GA算法与果蝇优化算法和遗传算法的结果进行比较,证明了其可行性和有效性.

Abstract:

According to the characteristics of the flexible job shop scheduling problems, an optimization model is established with the goal of minimizing the completion time, and a hybrid algorithm of fruit fly optimization algorithm and genetic algorithm (FOA-GA) is proposed. In the olfactory stage, local search technique is used to find the optimal path; In the visual stage, combining the crossover and competition mechanism of genetic algorithm, the information exchange between individuals is carried out, the mutation operator with an optimization tendency and the conventional mutation operator are used to carry out the two-part mutation, and then the adaptive dynamic transfer operator is introduced to accelerate the convergence rate. In the simulation, the results of FOA-GA algorithm are compared with those of fruit fly optimization algorithm and genetic algorithm to prove its feasibility and effectiveness.