基于Bloch球面搜索的量子蚁群优化算法

摘要

To enhance the optimization efficiency of ant colony algorithms,a quantum ant colony optimization algorithm based on Bloch spherical search is proposed.When this algorithm works,ants' locations are encoded by the qubits described on the Bloch sphere,the ants' target locations are determined according to the selected probability constructed by the pheromone and the heuristic information,and the ants' movement is realized with the rotation of the qubits on the Bloch sphere.To avoid premature convergence,the mutation is performed with the Hadamard gates.The pheromone and the heuristic information are updated in the new location of ants.The simulation results show that the proposed algorithm is superior to other quantum intelligent optimization algorithms in both the search capability and the optimization efficiency.%为提高蚁群算法的优化效率,提出一种基于Bloch球面搜索的量子蚁群优化算法.该算法用Bloch球面描述的量子比特对蚂蚁位置编码,用信息素强度和启发式信息构造的选择概率选择蚂蚁的移动目标,用量子比特在Bloch球面上的绕轴旋转实现蚂蚁移动,用Hadamard门实现变异以避免早熟收敛,在移动后的新位置完成信息素和启发式信息的更新.仿真结果表明该方法的搜索能力和优化效率优于其他量子智能优化算法.