Two-dimensional irregular layout method based on multi-subpopulation particle swarm optimization

Abstract

A two-dimensional irregular layout method based on an improved particle swarm algorithm comprises the following steps of: converting geometric figures of a sample wafer and a material into a train of two-dimensional coordinate intervals, judging whether the two-dimensional intervals of the sample wafer and the material are overlapped with a heuristic bottom-left searching algorithm to move the position of the sample wafer relative to the material, and executing an improved PSO (Particle Swarm Optimization) searching process. According to the method of dividing multiple subpopulations, the influence of subpopulation optimal solutions on particles in the subpopulations is added under the condition of not changing a parameter trending to the current optimal solution, when the iteration time of a particle swarm reaches the initially set maximum iteration time, the particles stop iteration, and the current globally optimal solution is obtained as the final layout scheme. The two-dimensional irregular layout method based on the improved particle swarm algorithm has good searching capability, and is high in searching speed, good in final solution and good in layout effect.

Description

technical field [0001] The invention relates to computer-aided nesting technology, in particular to a nesting method. Background technique [0002] At present, domestic and foreign layout methods for irregular parts are basically concentrated in heuristic modern optimization methods, mainly including simulated annealing algorithm, genetic algorithm, particle swarm algorithm (PSO) and so on. In practice, because the simulated annealing algorithm is affected by the annealing speed, the speed is fast, it is easy to fall into the local extremum, and the speed is slow, it is difficult to meet people's needs; the genetic algorithm is often "premature" and has poor local search ability. ; The PSO algorithm has a strong local search ability, but it is also easy to fall into the extreme value area and it is difficult to jump out. [0003] Nesting problems have a wide range of applications, such as sheet metal layout, glass tailoring, clothing tailoring, etc. Due to the large product...

AI Technical Summary

Problems solved by technology

In practice, because the simulated annealing algorithm is affected by the annealing speed, the speed is fast, it is easy to fall into the local extremum, and the speed is slow, it is difficult to meet people's needs; the genetic algorithm is often "premature" and has poor local search ability. ; PSO algorithm has a strong local search ability, but it is also easy to fall into the extreme value area and it is difficult to jump out

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