An improved genetic algorithm and its application to resolve cutting stock problem arc presented. It is common to apply simple genetic algorithm （SGA） to cutting stock problem, but the huge amount of computing of SG...An improved genetic algorithm and its application to resolve cutting stock problem arc presented. It is common to apply simple genetic algorithm （SGA） to cutting stock problem, but the huge amount of computing of SGA is a serious problem in practical application. Accelerating genetic algorithm （AGA） based on integer coding and AGA＇s detailed steps are developed to reduce the amount of computation, and a new kind of rectangular parts blank layout algorithm is designed for rectangular cutting stock problem. SGA is adopted to produce individuals within given evolution process, and the variation interval of these individuals is taken as initial domain of the next optimization process, thus shrinks searching range intensively and accelerates the evaluation process of SGA. To enhance the diversity of population and to avoid the algorithm stagnates at local optimization result, fixed number of individuals are produced randomly and replace the same number of parents in every evaluation process. According to the computational experiment, it is observed that this improved GA converges much sooner than SGA, and is able to get the balance of good result and high efficiency in the process of optimization for rectangular cutting stock problem.展开更多
The nesting problem involves arranging pieces on a plate to maximize use of material. A new scheme for 2D ir- regular-shaped nesting problem is proposed. The new scheme is based on the NFP (No Fit Polygon) algorithm a...The nesting problem involves arranging pieces on a plate to maximize use of material. A new scheme for 2D ir- regular-shaped nesting problem is proposed. The new scheme is based on the NFP (No Fit Polygon) algorithm and a new placement principle for pieces. The novel placement principle is to place a piece to the position with lowest gravity center based on NFP. In addition, genetic algorithm (GA) is adopted to find an efficient nesting sequence. The proposed scheme can deal with pieces with arbitrary rotation and containing region with holes, and achieves competitive results in experiment on benchmark datasets.展开更多
基金This project is supported by National Natural Science Foundation of China (No.50575153)Provincial Key Technology Projects of Sichuan, China (No.03GG010-002)
文摘An improved genetic algorithm and its application to resolve cutting stock problem arc presented. It is common to apply simple genetic algorithm （SGA） to cutting stock problem, but the huge amount of computing of SGA is a serious problem in practical application. Accelerating genetic algorithm （AGA） based on integer coding and AGA＇s detailed steps are developed to reduce the amount of computation, and a new kind of rectangular parts blank layout algorithm is designed for rectangular cutting stock problem. SGA is adopted to produce individuals within given evolution process, and the variation interval of these individuals is taken as initial domain of the next optimization process, thus shrinks searching range intensively and accelerates the evaluation process of SGA. To enhance the diversity of population and to avoid the algorithm stagnates at local optimization result, fixed number of individuals are produced randomly and replace the same number of parents in every evaluation process. According to the computational experiment, it is observed that this improved GA converges much sooner than SGA, and is able to get the balance of good result and high efficiency in the process of optimization for rectangular cutting stock problem.
基金Project (No. 60573146) supported by the National Natural ScienceFoundation of China
文摘The nesting problem involves arranging pieces on a plate to maximize use of material. A new scheme for 2D ir- regular-shaped nesting problem is proposed. The new scheme is based on the NFP (No Fit Polygon) algorithm and a new placement principle for pieces. The novel placement principle is to place a piece to the position with lowest gravity center based on NFP. In addition, genetic algorithm (GA) is adopted to find an efficient nesting sequence. The proposed scheme can deal with pieces with arbitrary rotation and containing region with holes, and achieves competitive results in experiment on benchmark datasets.
