基于文化知识的量子进化算法

A quantum-inspired evolutionary algorithm based on culture and knowledge

量子进化算法在实数优化时存在局部寻优能力不佳、收敛速度较慢等缺陷.为克服这些缺陷,本文引入文化算法思想提出一种基于文化知识的量子进化算法,该算法具有量子进化层和知识进化层双层进化框架,引入的文化算法能较好地协调全局与局部寻优,并避免算法陷入局部极值.由于新的算法框架及量子观测方式的引入,提出的算法不但保留了量子编码的优点,而且有效解决了求解实数优化问题时存在的缺陷.实验表明,提出的算法不但比量子进化类型算法性能有较大提升,而且与其它相关的几种算法相比具有更好的求解精度和速度.

Quantum-inspired evolutionary algorithm has premature and slow convergence shortcomings on solving numerical optimization problems. To overcome these shortcomings, a novel quantum-inspired evolutionary algorithm based on culture ＆ knowledge is proposed by introducing the cultural algorithm. This algorithm contains two evolutionary layers： quantum evolutionary layer and knowledge evolutionary layer. Since the introduction of cultural algorithm, this algorithm can achieve fine balance between ex- ploration and exploitation as well as can escape from local optimum. Because of the new framework and quantum observation, the proposed algorithm not only retains the advantages of quantum coding, but also effectively solves numerical optimization problems. The experimental results show that the algorithm has better performance than the quantum-inspired evolutionary algorithms. The proposed algorithm performs better than other related algorithms in terms of speed and accuracy.