An Adaptive Genetic Algorithm for Rectangular Layout of Defective Plate

Combining the lowest horizontal line algorithm with defect constraints and adaptive genetic algorithm to optimize the layout of rectangular parts with internal defective plates. The adaptive genetic algorithm is used to optimize the arrangement order of rectangular parts. It adaptively adjusts the crossover probability according to the individual fitness value, which can improve the calculation efficiency of the algorithm and avoid falling into the local optimal solution. Based on the lowest horizontal line algorithm with defect constraints, the lowest horizontal line is updated by judging the position of the defect, so that the rectangular part can effectively avoid the defective part during layout. The algorithm is programmed and simulated in the Python environment. Compared with the classic genetic algorithm, the proposed algorithm improves the maximum utilization rate of the board and the stability of layout optimization in the plate layout problem with two different numbers of defects. This shows that the lowest horizontal line algorithm with defect constraints based on the adaptive genetic algorithm has practical application value in the layout problem of defective plates.