Research on Steel Plate Path Optimisation Based on Genetic and Ant Colony Interaction Algorithm

In the steel plate cutting production process, path optimisation is crucial for improving cutting efficiency, reducing material costs and invalid cutting paths. In this paper, an interactive optimisation strategy combining genetic algorithm and ant colony algorithm is proposed to optimise the cutting sequence, overpass connecting structure, and empty distance of different distribution of steel plates by constructing a multi-objective mathematical model. The ant colony algorithm was used to solve the traveller's problem (TSP) path optimisation problem to optimise the cutting path of N1 steel plates. The improved genetic algorithm, on the other hand, handled the bridge crossing constraints for N4 steel plates and balanced the air travel distance with the number of bridges. The experimental results show that the ACO algorithm achieves better air-travel paths in N1 steel plates compared to the conventional optimisation algorithms, while the genetic algorithm significantly improves the material utilisation and satisfies all the geometrical constraints in the optimisation of N4 steel plates. The strength of this study lies in the contributory research in the related field, which validates the effectiveness and robustness of the proposed algorithms and provides an efficient solution to the complex steel plate cutting problem.