摘要
针对量子进化算法(Quantum–inspired Evolutionary Algorithm,QEA),在解决实际问题中遇到的困难,提出一种改进的量子进化算法,应用于求解旅行商问题(Travelling Salesman Problem,TSP),并提出了TSP中的Hamilton圈的随机搜索编码技术。通过求解TSP问题库中的部分问题,表明改进的算法比经典的量子进化算法及免疫遗传算法具有更快的收敛速度和更好的全局寻优能力。
Aiming at the difficulty faced by Quantum-inspired Evolutionary Algorithm （QEA） in solving actual problems, this paper presents an improved QEA, and applies it to the TSP.Combining with the improved QEA, an arbitrarily searching encoding method of Hamihonian cycle is proposed.The computation results of problems from TSP database indicate that the performance of improved QEA is superior to that of the conventional QEA and the immune evolutionary algorithm.
出处
《信息与电子工程》
2006年第6期412-416,共5页
information and electronic engineering
