量子蚁群算法求解多任务联盟问题

针对蚁群算法在求解多任务联盟问题(multi-task coalition problem,MTCP)时存在的求解精度不高、迭代次数多的不足,利用量子计算的并行性,提出了一种求解多任务联盟问题的量子蚁群算法.首先,利用量子叠加态给出了基于Agent的量子编码,使1个Agent能占据空间中的2个位置;其次,为使旋转角获得合适的大小和方向,提出了一种基于信息素的自适应修正旋转角调整策略;最后,通过对量子编码进行观测,给出了基于量子态的蚂蚁寻优策略.实验结果表明,与已有的算法相比,该算法不仅能获得更高质量的解,而且收敛速度也有显著的提高.

Quantum Ant Colony Algorithm for the Multi-task Coalition Problem

With concentration on the defects of ant colony algorithm as not-high precision and much iteration existing in the algorithm of multi-task coalition problem(MTCP),this dissertation utilized the characteristics of quantum computation simultaneously processing substantial quanta in parallel to put forward quantum ant colony algorithm on MTCP.First,it utilized quantum superposition states to give quantum code based on Agent,making each Agent occupy 2 positions in the space;Second,in order for the rotation angle to obtain proper size and direction,it posed the self-adaptive and corrective rotation angle adjusting strategy on the basis of pheromone;Finally,it gave ant algorithm-seeking strategy based on the quantum states by monitoring on the quantum code.Substantial simulation experiments show that,compared with existing algorithms,this one can not only obtain better algorithm,but also improve the convergence speed prominently.