基于改进量子遗传算法的连续函数优化研究  

Continuous function optimization based on improved quantum genetic algorithm

作　　者：朱筱蓉[1] 张兴华[1] 

机构地区：[1]南京工业大学自动化学院,江苏南京210009

出　　处：《计算机工程与设计》2007年第21期5195-5197,5301,共4页Computer Engineering and Design

基　　金：江苏省教育厅自然科学基金项目(06KJB510040)。

摘　　要：针对一般量子遗传算法在求解连续函数优化问题时存在的困难,研究了一种改进的量子遗传算法。该算法采用一种新的量子旋转门——H_ε门对种群进行更新操作,可有效避免算法陷入局部最优解,提高算法的全局寻优能力。将该算法应用于几个典型复杂函数的优化测试结果表明,改进的量子遗传算法在对连续函数进行求解时,综合性能明显优于传统遗传算法和一般量子遗传算法。Aimed at the difficulty in solving the continuous fimctions of general quantum genetic algorithm, an improved quantum genetic algorithm is studied. In this algorithm, HE gate, a novel quantum rotation gate, is adopted to update the population, which can prevent the algorithm from falling into local optimum, and improve the global searching ability of the algorithm. The test results indicate that, when this algorithm optimizes continuous fimction, its overall performance is superior to that of conventional genetic algorithm and general quantum genetic algorithm.

关 键 词：遗传算法 量子遗传算法 HE门 连续函数 优化 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]