摘要
提出了一种改进遗传算法求解 TSP.该方法在迭代初期引入不适应度函数作为评价标准 ,结合启发式交叉和边重组交叉算子设计了一种新的交叉算子 ,并对变异后个体进行免疫操作 .此外对操作后群体进行整理 ,删除群体中相同个体 ,得到规模为 N1的中间群体 ,对较优的 N -N 1个个体进行启发式变异 ,并将变异后个体补充进中间群体 ,生成规模为 N的新群体 ,这样保证群体中没有相同个体 ,从而保证群体多样性 .数值结果表明这种改进遗传算法是有效的 .
An improved genetic algorithm is presented for solving traveling salesman problems. This method introduces unfitness function as a criterion at the beginning of iteration, designs a new crossover operator, applies a hybrid mutation operator, and makes immune operation on individual after mutation operation. In addition, we recompose the population to insure that every individual in it is different. Morover we give a simple prove in theory. The simulation numerical results show that this algorithm is efficient.
出处
《数学的实践与认识》
CSCD
北大核心
2005年第2期129-133,共5页
Mathematics in Practice and Theory
