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针对无等待流水车间调度问题,提出了一种新颖的量子萤火虫优化算法用于最小化总完工时间.首先,将量子进化机制嵌入萤火虫算法中,并设计一种快速的局部邻域搜索方法,在每次迭代时只搜索部分邻域,同时采用目标增量计算邻域解变化,这样极大地加快了算法迭代速度,加速了算法收敛.最后,应用Taillard基准测试实例仿真,与目前较优的启发式算法IHA(improved heuristic algorithm)和群智能算法DGSO(discrete glowworm swarm optimization)、GA-VNS(genetic algorithm-variable neighborhood search)及DHS(discrete harmony search)相比较,产生最好解的平均百分比偏差均下降了40%以上.实验结果验证了所提算法在求解无等待流水调度中的优越性.
Aiming at the problem of no-wait flow shop scheduling, a novel quantum firefly optimization algorithm is proposed to minimize the total completion time.First, the quantum evolution mechanism is embedded in the firefly algorithm, and a fast local neighborhood search method is designed. In each iteration, only some neighborhoods are searched, and the target increment is used to calculate the neighborhood solution, which greatly accelerates the algorithm iteration speed and accelerates the convergence of the algorithm.Finally, with the Taillard benchmark test simulation, Compared with DGSO (discrete glowworm swarm optimization), GA-VNS (genetic algorithm-variable neighborhood search) and DHS (discrete harmony search), IHA (average heuristic algorithm) A decrease of more than 40% .The experimental results verify the superiority of the proposed algorithm in solving awaiting flow scheduling.