用于局部检测路径优化的遗传算法及其控制参数优化

GENETIC ALGORITHMS AND ITS CONTROL PARAMETER OPTIMIZATION USED FOR INSPECTION PATH OPTIMIZATION

在对传统算子分析的基础上，提出了一种对求解大规模路径优化问题很有效的杂交算子——贪婪选择杂交算子．该算子采用与优化问题直接相关的距离信息指导子代的产生过程，使新生成的子代路径在局部向着距离更短的方向发展．同时利用正交实验的方法对遗传算法中的控制参数进行了优化，大大提高了算法优化能力，而且缩短了程序运行的时间。

Local probe path is optimized with genetic algorithms. A novel crossover operator named greedy selection crossover is introduced, which generates the next generation according to the distance between every two inspection points and the distance is directly related to the final result. This algorithm is very effective for solving large scale path optimization problem. The measuring distance is shortened and the efficiency is improved significantly after optimization. The influence of control parameter for genetic algorithms is studied, values of control parameter for different scale inspection path optimization problem are given.