摘要
为提高遗传算法求解旅行商问题的效率,提出了一种改进量子交叉算子遗传算法.与经典量子全干扰交叉算子中城市的选择完全依赖于其位置的选择策略相比,新算子在选择城市时加入了父代优质解的有用信息,从而在维持解的多样性的同时,提高交叉所产生新解的质量.仿真算例结果表明,改进交叉算子遗传算法有着良好的全局搜索和局部挖掘能力,针对TSP问题的最优解、平均解均优于传统算法.
In order to improve the efficiency of Genetic Algorithm （GA） to Traveling Salesman Problem （TSP）, an improved quantum crossover is proposed in this paper. Compared with the traditional quantum crossover in which a city is selected according to the position, the new crossover selects a city depending on the distance comparing. The new cross- over can maintain the diversity of population and generate higher quality solutions. Simulation result shows that the im- proved quantum crossover based GA has good ability in global exploration and local exploitation. The best solution and the average solutions on TSP are all superior to those of traditional algorithm.
出处
《南京师范大学学报（工程技术版）》
CAS
2012年第3期43-48,共6页
Journal of Nanjing Normal University(Engineering and Technology Edition)
基金
淮海工学院自然科学基金(Z2011033
Z2011139)
关键词
旅行商问题
遗传算法
改进量子交叉
优化问题
traveling salesman problem （TSP）, genetic algorithm （GA）, improved quantum crossover, optimization problem
