Research on 2D Nesting Optimization Combining Improved no-fit polygon Method and Genetic Algorithm

This paper presents a two-dimensional nesting program that integrates an improved no-fit polygon method with an enhanced genetic algorithm, aimed at optimizing the arrangement of irregular convex polygons in sheet metal layout. The program leverages the global search capability of the genetic algorithm, iterating to enhance the nesting efficiency by encoding layout solutions and applying operations such as natural selection, crossover, and mutation. We introduced the concept of dynamic search spacing, allowing the algorithm to adjust the granularity of the search during iterations based on the number of iterations already performed, thereby accelerating convergence speed in the initial phase and enhancing the quality of solutions in the later phase. By employing the improved no-fit polygon method, the search range was narrowed. Through a series of benchmark tests, our algorithm demonstrated significant improvements in material utilization rates and maintained competitiveness in computational time. This study provides an efficient nesting strategy for sheet metal layout.