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工程设计学报
工程设计学报  2016, Vol. 23 Issue (2): 195-200    DOI: 10.3785/j.issn.1006-754X.2016.02.014
建模、分析、优化和决策     
基于粒子群遗传算法的泊车系统路径规划研究
王辉1, 朱龙彪1, 朱天成2, 陈红艳1, 邵小江1, 朱志慧3
1. 南通大学 机械工程学院, 江苏 南通 226019;
2. 中国联合通信网络有限公司 江苏省分公司, 江苏 南京 210024;
3. 江苏金冠立体停车股份有限公司, 江苏 南通 226003
Research on path planning of parking system based on PSO-genetic hybrid algorithm
WANG Hui1, ZHU Long-biao1, ZHU Tian-cheng2, CHEN Hong-yan1, SHAO Xiao-jiang1, ZHU Zhi-hui3
1. School of Mechanical Engineering, Nantong University, Nantong 226019, China;
2. Jiangsu Branch, China United Network Communications Group Co., Ltd., Nanjing 210024, China;
3. Jiangsu Jinguan Solid Parking System Engineering Co., Ltd., Nantong 226003, China
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摘要： 针对智能停车库自动导引运输车(automated guided vehicle, AGV)存取车路径规划问题,提出了一种基于粒子群和遗传算法的动态自适应混合算法.在标准粒子群算法和遗传算法的基础上,通过引入动态自适应调整策略分别对惯性权重系数、学习因子以及交叉变异概率公式进行了优化.在进化初期,通过在惯性权重系数和学习因子之间建立动态联动关系来实现对粒子速度和位置的实时有效更新;在进化后期,通过引入自适应遗传算法的交叉、变异操作来增强混合算法的全局搜索能力,提高算法的进化速度和收敛精度.为验证混合算法的可行性和有效性,选用MATLAB软件对其进行仿真测试.仿真测试结果显示,与禁忌搜索算法、蚁群算法以及遗传算法相比,混合算法表现出较强的全局搜索能力和较好的收敛性能,表明混合算法可行和有效.
关键词： 粒子群算法;  遗传算法;  泊车系统;  AGV;  路径规划    
Abstract: Aiming at path planning problem of AGV accessing cars, the dynamic adaptive hybrid algorithm was proposed by combining particle swarm optimization (PSO) with genetic algorithm (GA). Based on the standard PSO and GA, inertia weight coefficient, learning factor and the formula of crossover and mutation probability were optimized and improved with the dynamic adaptive adjustment strategy. In the initial stage of algorithm evolution, the dynamic linkage relation built between inertia weight coefficient and learning factor were utilized to deal with real-time updates of particle's velocity and position. In the later stage of evolution, in order to strengthen global search ability of hybrid algorithm and improve evolution speed and convergence precision of the algorithm, crossover and mutation operators of adaptive genetic algorithm (AGA) were introduced. Finally, MATLAB was used to verify the feasibility and effectiveness of hybrid algorithm. The simulation results showed the global search ability and convergence performance of hybrid algorithm were optimal by being compared with tabu search algorithm (TSA), ant colony algorithm (ACO) and GA. The conclusion indicates that the hybrid algorithm is feasible and effective.
Key words: particle swarm optimization    genetic algorithm    parking system    AGV    path planning
收稿日期: 2015-11-16 出版日期: 2016-04-28
CLC:  TP301.6  
基金资助:

国家自然科学基金资助项目(51405246);江苏省产学研联合创新基金资助项目(BY2014081-07).
通讯作者: 朱龙彪,男,教授,从事机电控制和故障诊断等研究,E-mail:zhulb@ntu.edu.cn.http://orcid.org//0000-0001-7105-4258     E-mail: zhulb@ntu.edu.cn
作者简介: 王辉(1989—),男,河南周口人,硕士生,从事机电控制和智能算法研究,E-mail:whzl2014@126.com.http://orcid.org//0000-0002-0563-5801
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引用本文:

王辉, 朱龙彪, 朱天成, 陈红艳, 邵小江, 朱志慧. 基于粒子群遗传算法的泊车系统路径规划研究[J]. 工程设计学报, 2016, 23(2): 195-200.

WANG Hui, ZHU Long-biao, ZHU Tian-cheng, CHEN Hong-yan, SHAO Xiao-jiang, ZHU Zhi-hui. Research on path planning of parking system based on PSO-genetic hybrid algorithm. Chinese Journal of Engineering Design, 2016, 23(2): 195-200.

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https://www.zjujournals.com/gcsjxb/CN/10.3785/j.issn.1006-754X.2016.02.014    或    https://www.zjujournals.com/gcsjxb/CN/Y2016/V23/I2/195

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