基于逆转算子的遗传算法在螺母紧固路径中的优化

摘要

Optimization path of tightening nut on the assembly line is a non-deterministic polynomial complexity problem, the NP ( Non-deterministic Polynomial) complete problem. Because the time complexity of the problem increases with the size of the prob-lem exponential y, an efficient polynomial-time algorithm is not found to optimize the robot running path by now.For this, this article introduces reversed operator into improving the genetic algorithm and deeply making a study of the optimization of the running path, for the manipulators, and the MATLAB simulation of the nut tightening path. The simulation results indicate that the improved genetic algorithm can be used to step over the local minimum, and find the optimal values fastly and stably.%装配生产线上螺母紧固优化路径属于多项式复杂程度的非确定性问题，即NP ( non-deterministic polynomial)完全问题，由于该问题的时间复杂度随着问题规模的增大按指数方式增长，到目前为止还未找到一个有效的多项式时间算法，对机械手的运行路径进行优化。针对该问题，引入逆转算子对遗传算法进行改进，进而对机械手的运行路径优化问题进行了深入研究，并对螺母紧固优化路径进行了MATLAB仿真对比研究，仿真结果表明该改进遗传算法能很好的跳出局部极小值，并能快速、稳定的寻找到最优值。