基于新型生物地理学优化算法的作业车间调度研究

Research on job shop scheduling based on newbiogeography-based optimization algorithms

针对生物地理学优化算法在求解复杂作业车间调度问题时存在的问题,提出了一种改进差分进化生物地理学优化算法.通过将差分进化算法的搜索性与生物地理学优化算法的利用性有效的结合,同时采用精英保留机制保留适应度较高的个体,并且引入惯性权重策略调节变异操作在混合迁移操作中所占的比重以提高算法的全局搜索能力,然后增加了小概率扰动以防止算法随着迭代的进行陷入局部最优解.最后使用不同测试函数和作业车间调度问题进行实验,结果显示改进算法在收敛速度和优化结果方面性能更优。

Aiming at the problems of biogeography-based optimization algorithm(BBO) in solving complex job shop scheduling problems(JSP), an improved differential evolution biogeography-based optimization algorithm is proposed. By effectively combining the searchability of differential evolution algorithm(DE) with the utilization of biogeography-based optimization algorithm, at the same time, elite retention mechanism is adopted to retain individuals with higher fitness, and inertial weight strategy is introduced to adjust the proportion of mutation operation in hybrid migration operation to improve the global search ability of the algorithm, then increase the disturbance in the small probability in order to prevent the algorithm as the iteration progressed into a local optimal solution. Finally, different test functions and job shop scheduling problems are used for experiments. The results show that the improved algorithm has better performance in convergence speed and optimization results.