Genetic algorithm based on reinforcement learning for a novel drilling path optimization problem

Hole-making is one of the basic processes of mechanical production. For the problem of toolpath optimization of computer numerical control (CNC) machine tools, a novel toolpath model for hole-making called as multi-tool drilling path optimization problems with decidable holes (MTdDPO) is proposed. In the MTdDPO, holes on workpieces are divided into two categories: fixed holes and decidable holes. The goal of the MTdDPO is to minimize the length of the machining path by judging the path ownership of decidable holes and the machining sequence of all holes in each path. To realize the optimization of the MTdDPO, a segmented genetic algorithm based on reinforcement learning (RLSGA) is proposed. The population of the RLSGA is regarded as the agent, the states of the agent are the intervals of the diversity coefficient of the population, three different segmental crossover operators are the actions of the agent, and the reward of the agent is related to the changes in fitness value and diversity coefficients of the population. Based on the MTdDPO, 5 benchmark test problems are designed, and the RLSGA is compared with other 4 algorithms on these test problems. Results show that the performance of the RLSGA is significantly better than other algorithms, which means the RLSGA can effectively solve the MTdDPO problems. © 2024 Northeast University. All rights reserved.