求解多堆垛机协同调度优化问题的量子蝙蝠算法

Quantum-based bat algorithm for solving collaborative scheduling optimization of multi-stacker

针对肉鸡仓储式养殖给料调度优化问题,以多堆垛机协同给料完成时间最短为优化目标,建立多堆垛机协同给料模型,并基于蝙蝠算法(BA)对其进行求解。然而,该算法存在易早熟、收敛速度慢等缺点,为此,借用量子理论,引入启发式量子变异,提出量子蝙蝠算法(QBA),主要通过对非最优个体采用量子旋转门策略及自适应调整旋转角机制实现变异,从而增加了种群多样性,提高了全局寻优能力及求解效率。最后,通过仿真实例验证了该算法的有效性。

Aiming at the warehouse feeding scheduling optimization problem of the broiler breeding,taking the shortest completion time of feeding as the optimizing objective,the collaborative feeding mathematical model of multi-stacker is built,and a heuristic algorithm based on bat algorithm( BA) is used to solve the problem. To overcome the shortcomings of prematurity and the slow convergence speed in the standard bat algorithm,quantum-based bat algorithm( QBA) based on heuristic quantum mutation is proposed. The individual mutation is realized to increase the diversity of the population by introducing quantum rotation gate strategy and adaptive adjusting rotation angle mechanism,which improves greatly the global optimizing ability and efficiency. Finally,the simulation examples are presented to validate the algorithm.