改进量子遗传算法在多峰值函数寻优中的应用

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 pa-per 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 con-vergence speed;and adds quantum immigration and elitist selection strategy to improve the planning efficiency and avoid fall-ing into local optimal.Then the paper uses complex function of two variables to test the improved quantum genetic algo-rithm,and the result proves that the improved quantum genetic algorithm has higher efficiency.