摘要
为了提高全局寻优能力和收敛速度,基于量子进化算法和混合遗传算法,提出了一种新的进化算法.该算法将下降搜索理论应用到量子进化算法中,改进了量子进化算法仅靠量子门进行迭代的作用,从而加快了收敛速度,并降低了个体在进化时产生退化的可能性.典型函数的仿真实验结果表明,该算法具有好的全局性和收敛性.
To raise global search capacity and convergent speed, a new evolution algorithm, based- descending search quantum evolution algorithm, was put forward on the basis of the quantum evolution algorithm (QEA) and the hybrid genetic algorithm. In the proposed algorithm, the descending search theory of optimization principles is applied, so the iterative effect, only relying on quantum gate, of QEA is improved to speed up the convergent speed, and the possibility of individual retrogression in the evolution process is reduced. The simulation result of a typical function shows that this algorithm has a good convergence performance and global search capacity.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2004年第3期390-393,共4页
Journal of Southwest Jiaotong University
关键词
优化
进化算法
量子进化算法
下降搜索
optimization
evolution algorithm
quantum evolution algorithm
descending search
