【摘要】 针对城市垃圾回收路径规划问题,提出了一种量子布谷鸟协同搜索算法,用于优化最短路径.首先,采用Bloch球面坐标量子编码来扩大解空间;然后设计了一种基于差分进化的量子布谷鸟搜索策略,实现较差个体的改进以及劣势个体与优势个体之间的信息交换,增强全局搜索能力;最后,利用一种局部邻域搜索算法进一步提高解的质量.理论分析了所提算法的收敛性.基于无线传感网络采集数据进行了仿真实验,将量子布谷鸟协同搜索算法与传统遗传算法和量子布谷鸟搜索算法分别比较,求解垃圾回收最短路径问题的最优解和平均解均改进了20%～40%,结果证明了量子布谷鸟协同搜索算法的优越性.更多还原

【Abstract】 A quantum-inspired cuckoo co-search algorithm is proposed to optimize the shortest path for garbage collection.First,the Bloch spherical coordinate quantum coding is used to enlarge the solution space.Then,a quantum cuckoo search strategy based on differential evolution is designed to realize the improvement of poor individuals and the information exchange between the inferior and dominant individuals.This strategy can enhance the global searching ability.Finally,a local neighborhood search algorithm is developed to further improve the quality of the solution.The convergence of the proposed algorithm is shown via a theoretical analysis.The simulation experiment is based on wireless sensor networks.Compared with the traditional genetic algorithm and the standard quantum-inspired cuckoo search algorithm,the proposed algorithm for the optimal solution and average solutions with the shortest path for garbage collection are improved by 20% ～ 40%,which proves the superiority of the proposed algorithm.更多还原