一种新的混合量子进化算法

量子进化算法(QEA)用于多峰函数优化时,容易陷入局部最优.本文提出一种新的混合量子进化算法,通过双编码机制(经典二进制编码和量子概率编码),以及经典交叉和量子概率编码更新策略,实现了经典遗传算法与量子进化算法的有机结合,在发挥经典遗传算法全局优化能力的同时,利用量子概率搜索提高了算法的局部搜索能力.通过一组典型函数优化实验对该算法的性能进行了考察,并与QEA进行了比较.结果表明,本文算法在解的质量和收敛速度上都要优于QEA.

A New Hybrid Quantum Evolutionary Algorithm

When quantum inspired evolution algorithm(QEA)is used for the optimization of continuous functions with many local optima,it's easy to be trapped into the local deceptive optima.In this paper,a new hybrid quantum evolution algorithm is proposed to overcome the shortcoming of the QEA.The new hybrid QEA combines the merits of classic GA and QEA by using double coding mechanism(classic binary coding and quantum probabilistic coding),and combining the classical crossover and quantum probabilistic search.It not only has the global searching capacity of classic GA,but also improves the local searching capacity of algorithm by using quantum probabilistic search.Experiments on 23 test functions of diverse complexities are implemented and compared with QEA in this paper.The result indicates that the new hybrid QEA is better than QEA in both quality of final result and the convergence rate.