改进量子遗传算法在多峰值函数寻优中的应用  

Application of Modified Quantum Genetic Algorithm in Optimization of Multi-peak Functions

作　　者：米兰 鱼佳欣 李东涛 谢瑞莎 

机构地区：[1]中国洛阳电子装备试验中心,河南济源459000 [2]国网洛阳供电公司,河南洛阳471000

出　　处：《计算技术与自动化》2015年第1期73-75,共3页Computing Technology and Automation

摘　　要：针对标准量子遗传算法(QGA)在寻找多峰值最优时存在局部寻优能力较差和易早熟的缺陷,提出一种改进量子遗传算法(QQGA),运用基于概率划分的小生境协同进化策略初始化量子种群,并采用动态量子旋转角调整策略来加快收敛速度;加入量子移民和保优选择策略,提高规划效率,避免陷入局部最优。利用复杂二元函数测试改进量子遗传算法,结果比标准量子遗传算法效率高。According to has the poor local searching ability and precocity in search of multi peak optimization,so this paper proposed an improved quantum genetic algorithm（QQGA）,which uses the probability of evolutionary strategy with niche to initiate the quantum population,and the dynamic quantum rotating angle adjustment strategy to speed up the convergence speed;and adds quantum immigration and elitist selection strategy to improve the planning efficiency and avoid falling into local optimal.Then the paper uses complex function of two variables to test the improved quantum genetic algorithm,and the result proves that the improved quantum genetic algorithm has higher efficiency.

关 键 词：量子遗传算法 多峰值函数 优化 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]