| 求解AGV路径优化问题的遗传算法参数优化 |
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| 引用本文： | 李莉,张立明,詹跃东.求解AGV路径优化问题的遗传算法参数优化[J].昆明理工大学学报(理工版),2006,31(4):26-29,38. |
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| 作者姓名： | 李莉 张立明 詹跃东 |
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| 作者单位： | 1. 昆明理工大学,机电工程学院,云南,昆明,650093
2. 昆明大学,电子信息与机械工程系,云南,昆明,650094 |
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| 基金项目： | 云南省自然科学基金(项目编号:2003F0029M) |
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| 摘 要： | 介绍了基于AGVS的有向图模型求解AGV路径优化问题的遗传并行路径规划算法和有关遗传算子.根据遗传算法的运行流程,首先对AGV路径进行初始路径集生成和确定复制算子;其次用实验的方法对交叉算子和变异算子进行了性能比较,确定AGV路径优化中选用部分交叉算子和反转变异算子;最后研究了种群的大小对遗传算子收敛速度的影响.本文给出了部分遗传算子的实验数据和不同种群规模时的收敛情况.本文工作是研究AGV动态调度遗传算法及其仿真与实验的基础.
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| 关 键 词： | AGVS 路径优化 遗传算法 |
| 文章编号： | 1007-855X（2006）04-0026-04 |
| 收稿时间： | 2005-11-14 |
| 修稿时间： | 2005-11-14 |
Optimization of Parameters of Genetic Algorithm for AGV Path Optimization |
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| LI Li,ZHANG Li-ming,ZHAN Yue-dong.Optimization of Parameters of Genetic Algorithm for AGV Path Optimization[J].Journal of Kunming University of Science and Technology(Natural Science Edition),2006,31(4):26-29,38. |
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| Authors: | LI Li ZHANG Li-ming ZHAN Yue-dong |
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| Affiliation: | 1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650093, China; 2. Department of Electrical Information and Mechanical Engineering, Kunming University, Kunming 650094, China |
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| Abstract: | Based on the directed graph models of Automated Guided Vehicle System(AGVS),an introduction is made on the parallel path planning,the related parameters and operators of genetic algorithm in order to get the optimum path for AGV.According to the running procedures of genetic algorithm,first of all,the generating of initial path sets for AGV paths has been carried out,and the reproduction operator is determined.Second,with the experimental methods,the characteristics of the crossovers and mutations of genetic algorithm are compared with each other,and the part crossover and inversion mutation have been selected in the optimum path for AGV.Finally,the population scales influencing the weakening speed of genetic algorithm are studied.Experimental data of some genetic operators and the converging conditions of genetic algorithm as different population scales are given.The achievements will be the foundations of studying the dynamic dispatch genetic algorithm and its simulation and experiment for AGV. |
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| Keywords: | Automated Guided Vehicle System path optimization genetic algorithm |
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