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针对柔性作业车间调度问题,提出了一种有效的混合分布估计算法.算法采用基于排序的编码和解码方法.为了保持种群多样性,采用k-均值聚类方法对种群进行分簇,从各子簇中选取具有代表性的若干个体组成优势种群以建立描述问题解空间分布的概率模型,该优势种群包含了全局统计信息及个体特征信息,利用变邻域搜技术优化种群中的最佳个体,避免其陷入局部最优.最后,通过算例仿真,表明算法具有良好的全局搜索能力和局部求精能力.
In order to solve the problem of flexible job shop scheduling, an effective hybrid distribution estimation algorithm is proposed. The algorithm uses a sort-based coding and decoding method.In order to maintain the diversity of population, k-means clustering method is used to cluster the population, In the cluster, several representative individuals are selected to form the dominant population to establish a probability model describing the spatial distribution of the problem solution. The dominant population contains the global statistical information and individual characteristic information, and uses the variable neighborhood search technique to optimize the best individual in the population, To avoid its falling into local optimum.Finally, the simulation results show that the algorithm has good global search ability and local refinement ability.