基于相位编码的量子蚁群算法  

Phase encoded-based quantum ant optimization

作　　者：李盼池[1,2] 宋考平[1] 杨二龙[1] 

机构地区：[1]东北石油大学石油与天然气工程博士后科研流动站,大庆163318 [2]东北石油大学计算机与信息技术学院,大庆163318

出　　处：《系统工程理论与实践》2011年第8期1565-1570,共6页Systems Engineering-Theory & Practice

基　　金：国家自然科学基金(60773065);国家博士后科学基金(20090460864;201003405);黑龙江省博士后基金(LBH-Z09289);黑龙江省教育厅科学技术基金(11551015)

摘　　要：针对蚁群算法只适用于离散优化问题的局限性和收敛速度慢的问题,提出一种适合连续优化的量子蚁群算法.该方法直接采用量子位的相位对蚂蚁编码.首先基于信息素强度和可见度构造的选择概率,选择蚂蚁的前进目标;然后采用量子旋转门更新描述蚂蚁位置的量子比特,完成蚂蚁移动;采用Pauli-Z门实现蚂蚁的变异增加位置的多样性;最后根据移动后的新位置完成蚁群信息素强度和可见度的更新.由于优化过程统一在空间[0,2π]~n进行,而与具体问题无关,因此,对不同尺度空间的优化问题具有良好的适应性.以函数极值优化和聚类优化为例,仿真结果表明该方法的搜索能力和优化效率明显优于普通蚁群算法和标准遗传算法.Aiming at the shortcoming of ant colony optimization which was only suitable for the discrete optimization and the problem of slow convergence,a suitable quantum ant colony optimization algorithm for continuous optimization was proposed.The locations of ant were directly encoded by the phase of qubits in the proposed algorithm.First,the destination to move was determined according to the select probability constructed by the pheromone information and heuristic information,then the qubits of ant were updated by quantum rotation gates to achieve ant moving,and mutated by quantum Pauli-Z gates to increase diversity of ants.Finally,the pheromone information and the heuristic information were updated in the new location of ants.As the optimization is performed in[0,2π]~n,which has nothing to do with the specific issues,hence,the proposed method has good adaptability for a variety of optimization problems. With applications of function extremum and clustering optimization,the simulation results show that the proposed algorithm is superior to the common ant colony optimization algorithm and the standard genetic algorithm in both search capability and optimization efficiency.

关 键 词：量子蚁群优化 相位编码 优化算法 连续优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]