摘要
在一般遗传算法中,求最优解时既可避免早熟收敛,又能提高收敛速度是困难的,因为算法中使用了单独一组交叉算子/变异算子。本文提出一种新的基于混合算子的遗传算法执行策略。在求解旅行商问题(TSP)中,为了提高局部搜索能力和收敛速度,给出了一种基于边重组的启发式交叉算子。仿真实验表明了这种算法的有效性。
In a general Genetic Algorithms （GA）, it is difficulty to avoid prematurely convergence and raise the speed of the algorithm convergence for complex finding the optimal solution, in which the algorithm is run with a single set of crossover/mutation operators. In this paper, a new run-strategy of Genetic Algorithms based on mixed genetic operators is presented, a heuristic crossover operator based on the edge recombination is also given to raise the ability of the local searching and the speed of convergence in solving the Traveling Salesman Problems （TSP）. The efficiency of the algorithm has been shown by simulative experiments.
出处
《计算机科学》
CSCD
北大核心
2007年第4期222-224,共3页
Computer Science
关键词
遗传算法
遗传算子
全局优化
早熟收敛
旅行商问题(TSP)
Genetic algorithms, Genetic operators, Global optimization, Prematurely convergence, Traveling Salesman Problems （TSP）
