Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (10): 2905-2911.DOI: 10.11772/j.issn.1001-9081.2020121994

Special Issue: 先进计算

• Advanced computing • Previous Articles     Next Articles

Improved grey wolf optimizer for location selection problem of railway logistics distribution center

HAO Pengfei, CHI Rui, QU Zhijian, TU Hongbin, CHI Xuexin, ZHANG Diyou   

  1. College of Electrical and Automation Engineering, East China Jiaotong University, Nanchang Jiangxi 330013, China
  • Received:2020-12-17 Revised:2021-04-10 Online:2021-10-10 Published:2021-07-14
  • Supported by:
    This work is partially supported by the Regional Program of National Natural Science Foundation of China (51567008, 61961018), the Natural Science Foundation of Jiangxi Province (20181BAB202017), the Youth Project of Science and Technology Research of Education Department of Jiangxi Province (GJJ190354, GJJ190295), the Provincial Key Project of Jiangxi Province Innovation and Entrepreneurship College Student Training Program (202010404011s).

求解铁路物流配送中心选址问题的改进灰狼优化算法

郝芃斐, 池瑞, 屈志坚, 涂宏斌, 池学鑫, 张地友   

  1. 华东交通大学 电气与自动化工程学院, 南昌330013
  • 通讯作者: 池瑞
  • 作者简介:郝芃斐(1999-),女,山西太原人,主要研究方向:智能优化、优化算法;池瑞(1985-),女,河南南阳人,讲师,博士,主要研究方向:智能优化、多目标优化;屈志坚(1978-),男,江西南昌人,教授,博士,主要研究方向:电力大数据、智能算法;涂宏斌(1979-),男,江西南昌人,副教授,博士,主要研究方向:模式识别、优化算法;池学鑫(1984-),男,湖北随州人,硕士,主要研究方向:智能控制;张地友(1999-),男,江西景德镇人,主要研究方向:智能优化、智能算法。
  • 基金资助:
    国家自然科学基金地区科学基金资助项目(51567008,61961018);江西省自然科学基金资助项目(20181BAB202017);江西省教育厅科学技术研究青年项目(GJJ190354,GJJ190295);江西省创新创业大学生训练计划省级重点项目(202010404011s)。

Abstract: The single mechanism based Grey Wolf Optimizer (GWO) is easy to fall into local optimum and has slow convergence speed. In order to solve the problems, an Improved Grey Wolf Optimization (IGWO) was proposed to solve the actual location selection problem of railway logistics distribution center. Firstly, based on the basic GWO, the theory of good point set was introduced to initialize the population, which improved the diversity of the initial population. Then, the D-value Elimination Strategy (DES) was used to increase the global optimization ability, so as to achieve an efficient optimization mode. The simulation results show that, compared with the standard GWO, IGWO has the fitness value increased by 3%, and the accuracy of the optimal value increased by up to 7 units in 10 test functions. Compared with Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm and Genetic Algorithm (GA), IGWO has the location selection speed increased by 39.6%, 46.5% and 65.9% respectively, and the location selection velocity is significantly improved. The proposed algorithm can be used for railway logistics center location selection.

Key words: railway transportation, logistics, location selection of distribution center, Grey Wolf Optimizer (GWO), good point set

摘要: 针对单一机制的灰狼优化算法(GWO)易陷于局部最优、收敛速度慢的问题,提出了一种改进灰狼优化(IGWO)算法来解决实际铁路物流配送中心选址的问题。首先,在基本的灰狼优化算法的基础上,引入佳点集理论初始化种群,从而提高了初始种群的多样性;然后,利用差值剔除策略(DES)来增加全局寻优能力,以达到一种高效的寻优模式。仿真实验结果表明:与标准的灰狼算法相比,所提出的IGWO适应度值提高了3%,在10个测试函数中最优值精度可最多提高7个单位;与粒子群优化(PSO)算法、差分进化(DE)算法和遗传算法(GA)比较,所提算法的运行速度分别提高了39.6%、46.5%和65.9%,选址速度也明显提高。可见所提算法可用于铁路物流中心的选址。

关键词: 铁路运输, 物流, 配送中心选址, 灰狼优化算法, 佳点集

CLC Number: