摘要
遗传算法(GA)是由遗传进化理论指导的随机搜索寻优算法,传统GA的寻优能力与随机搜索能力之间存在着相互制约的关系,所以对地形极其复杂、极无规律的TSP的应用效果并不十分理想.通过利用互换启迪交叉算子加快局部搜索算法的收敛速度,利用模式增加修补算子防止算法早熟收敛,给出了一种求解TSP问题的新型遗传算法.仿真实验表明该算法是有效的和可行的.
Genetic Algorithms （GA）is a random search algorithm optimization instructed by the heredity evolution theory.But the relations between the superior ability and the stochastic search ability of traditional GA is mutually restrictive,therefore,it is not ideally effective for the TSP with the extremely complex,disordered terrain .Through the use of swap inspiration crossover operator to accelerate the convergence of local search algorithm and the use of increasing pattern patching operator to prevent premature convergence,this paper gives an improved genetic algorithm for solving traveling salesman problems.Simulation experiments show that the method is effective and feasible.
出处
《河南科技学院学报》
2010年第1期86-89,共4页
Journal of Henan Institute of Science and Technology(Natural Science Edition)
关键词
遗传算法
互换启迪交叉算子
模式增加修补算子
TSP
genetic algorithms
swap inspiration crossover operator
increasing pattern patching operator
traveling salesman problem
