Abstract: According to the characteristics of the flexible job shop scheduling problem, an improved biogeography-based optimization algorithm is proposed in this paper. The program uses a combination of the machine-based and order-based coding mechanism, at the same time superior individuals are generated based on heuristic rules in the initial population. Migration and mutation mechanism is improved based on standard biogeography-based optimization algorithm, in line with the scheduling problem of mobility model and adaptive mutation mechanism, for overcoming the shortcoming of early mature and slow convergence of traditional algorithms. Through simulation and comparison experiments, the results demonstrate the feasibility and effectiveness of the algorithm.

Key words: flexible job-shop scheduling problem, biogeography-based optimization algorithm, heuristic rules, adaptive

摘要： 针对柔性作业车间调度问题的特点，提出了一种基于改进生物地理学优化算法的求解方案。该方案采用基于工序和基于机器相结合的编码机制，在初始种群中引入启发式算法生成的优良个体，并在标准生物地理学算法基础上对迁移和变异操作进行了改进，采用符合该调度问题的迁移率模型和自适应变异机制，克服了传统算法易于早熟或收敛慢的缺点。通过仿真验证了该算法的可行性和有效性。

关键词: 柔性作业车间调度, 生物地理学优化算法, 启发式规则, 自适应