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针对自动化仓库的拣选作业调度问题,提出了一种多种群果蝇优化算法。采用随机键编码方式,利用味道浓度判定值的大小次序来映射调度解。通过同时学习子种群的局部最优和全局最优个体,实现对果蝇个体的更新计算。为了避免陷入局部最优,采用了一种果蝇个体变异机制。计算结果显示,多种群果蝇优化算法在计算精度和收敛效率方面要好于基本果蝇优化算法,并且搜索过程能够有效跳出局部最优。
Aiming at the problem of picking job scheduling in automated warehouses, a multi-species fruit flies optimization algorithm is proposed. Using the random key coding method, the scheduling solution is mapped by the order of the size of the taste determination value. The updated calculation of Drosophila individuals is achieved by simultaneously learning the local optimal and global optimal individuals of the subpopulation. In order to avoid falling into the local optimum, a mutation mechanism of Drosophila individuals was adopted. The results show that the multi-species fruit flies optimization algorithm is better than the basic fruit flies optimization algorithm in the calculation accuracy and convergence efficiency, and the search process can effectively jump out of the local optimum.