基于信息熵的量子免疫遗传算法

Quantum immune genetic algorithm based on information entropy

针对目前的量子进化算法在高维函数优化时容易陷入局部最优,利用信息熵的概念,将量子进化算法和免疫遗传算法进行改进与融合,提出一种基于信息熵的量子免疫遗传算法.该方法对抗体采用相位编码,用信息熵准确地度量量子比特的不确定信息;提出了一种按变量的种群熵降序排列的邻域搜索策略;对于抗体之间的相似度,给出了一种按个体熵相同变量位数的度量方法;用繁殖概率对抗体的多样性进行评价,并分别以函数优化问题和VRPSDP问题进行了仿真验证.研究结果表明:该算法收敛速度快,求解精度高.

Currently,high-dimensional function is optimized through quantum evolutionary algorithm,and it is easy to fall into local optimum.In this paper,the authors introduce the concept of information entropy and improve and integrate the fusion quantum evolutionary algorithm and immune genetic algorithm to propose a new quantum immune genetic algorithm based on information entropy.In this proposed algorithm,the antibody is encoded according to the phase,and uncertain information is measured by information entropy accurately.The authors propose a neighborhood search strategy by descending order of population entropy.For the degree of similarity between the antibodies,the authors give a measure based on the same individual entropy variable median,and the antibody diversity is evaluated through reproduction probability.The function optimization and VRPSDP（Vehicle Routing Problem with Simultaneous Delivery and Pickup,VRPSDP） are simulated and validated.The results show that the algorithm achieves a fast speed in convergence and accuracy in solution.