论文部分内容阅读
将混沌搜索的遍历性和量子计算的高效性融合到免疫优化中,提出一种用于连续空间优化的混沌量子免疫算法.该方法用量子位编码初始群体,用量子旋转门实现个体更新,在量子旋转门中引入2种幅值不同的混沌变量改变转角的大小.小幅值混沌变量用于优良个体的克隆扩增,实现局部搜索;大幅值混沌变量用于较差个体的突变,实现全局搜索.并证明算法的收敛性.实验表明,该算法能有效改善免疫优化算法的搜索能力和效率.
In this paper, chaos quantum immune algorithm is used to optimize chaos quantum immune algorithm by using the ergodicity of chaotic search and the efficiency of quantum computation. The chaotic quantum immune algorithm for continuous space optimization is proposed. In the quantum revolving door, two kinds of chaos variables with different amplitudes are introduced to change the rotation angle.The small-amplitude chaos variable is used for clonal amplification of excellent individuals to realize local search, and the large-amplitude chaotic variable is used to mutate the poorer individuals to realize the global Search and prove the convergence of the algorithm.The experiment shows that the algorithm can effectively improve the search ability and efficiency of the immune optimization algorithm.