一种基于克隆选择的量子遗传函数优化算法

为了解决量子遗传算法(QGA)用于连续多峰函数优化易陷入局部极值的问题,将免疫学中的克隆选择算法的概念和原理引入到量子遗传算法中,提出了一种新型的进化算法——基于克隆选择的量子遗传函数优化算法.该算法通过克隆选择、高斯变异以及量子旋转门等操作对可行解进行搜索,提高了算法在解决函数优化问题的全局寻优能力。典型函数的测试结果表明该算法优于传统的QGA和一些遗传算法。

A Quantum Genetic Function Optimization Algorithm Based on Clonal Selection

In order to overcome the shortcoming of the quantum genetic algorithm(QGA),in which the result may converge to the local optimal value,when it is used in the optimization of continuous functions with many extreme values,the concept and principle of clonal selection in immunology is introduced.A novel evolutionary algorithm i.e.the QGA based on the clonal selection is proposed.By using the clonal selection,the Gauss mutation and the quantum rotate gate,the algorithm can search the feasible solution.The performance of the global optimal exploration for solving the function optimization problem is improved.The test results from typical functions show that the performance with this algorithm is better than the conventional QGA and some other genetic algorithm.