FANG Hui

YIN Guofu

LI Haiqing

PENG Biyou
School of Manufacturing Science
and Technology,
Sichuan University,
Chengdu 610065, China

Abstract: An improved genetic algorithm and its application to resolve cutting stock problem are 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.

Key words: Accelerating genetic algorithm  Efficiency of optimization  Cutting stock problem