Research on Expressway Emergency Vehicle Allocation Based on Improved Particle Swarm Optimization

被引:2
|
作者
Wu, Lieyang [1 ]
机构
[1] Jiangxi Expressway Networking Management Ctr, Nanchang 330036, Jiangxi, Peoples R China
来源
关键词
Particle swarm optimization; Population diversity; Expressway; The allocation of emergency vehicles;
D O I
10.1007/978-3-319-48490-7_17
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Firstly, an improved particle swarm optimization algorithm is proposed to solve the allocation of expressway emergency vehicles. Compared with the standard PSO algorithm, the particle population number of the improved PSO algorithm is increased, due to particle flight behavior of different populations is different and particle information between different populations is exchanged, so the swarm population diversity of the improved PSO algorithm is increased, and its ability to jump out of local optimum is improved. Moreover, the improved algorithm is applied to the allocation of emergency vehicles, that is, the mathematical model is established to solve the shortest travel distance of the emergency vehicle, and the mathematical model is optimized by the proposed algorithm to obtain the optimal solution. The experimental results show that the improved algorithm proposed in this paper is feasible and effective to solve the expressway emergency vehicle allocation problem.
引用
收藏
页码:139 / 145
页数:7
相关论文
共 50 条
  • [41] An Algorithm Based on the Improved Particle Swarm Optimization
    Ge, Ri-Bo
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, KNOWLEDGE ENGINEERING AND INFORMATION ENGINEERING (SEKEIE 2014), 2014, 114 : 176 - 179
  • [42] Research on Speed Optimization Strategy of Hybrid Electric Vehicle Queue Based on Particle Swarm Optimization
    Wang, Shaohua
    Yu, Chengquan
    Shi, Dehua
    Sun, Xiaoqiang
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2018, 2018
  • [43] Scrum Task Allocation Based on Particle Swarm Optimization
    Brezocnik, Lucija
    Fister, Iztok, Jr.
    Podgorelec, Vili
    BIOINSPIRED OPTIMIZATION METHODS AND THEIR APPLICATIONS, BIOMA 2018, 2018, 10835 : 38 - 49
  • [44] Parameters Optimization for Extended-range Electric Vehicle Based on Improved Chaotic Particle Swarm Optimization
    Jiang, Yongchen
    Lin, Cheng
    Cao, Wanke
    INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING, 2016, 9 (09): : 1 - 10
  • [45] The Research and Application of BP Neural Network Based on Improved Particle Swarm Optimization
    Huang, Dechang
    Huang, Zhaodi
    Zhou, Jiali
    Wang, Yifan
    NEW INDUSTRIALIZATION AND URBANIZATION DEVELOPMENT ANNUAL CONFERENCE: THE INTERNATIONAL FORUM ON NEW INDUSTRIALIZATION DEVELOPMENT IN BIG-DATA ERA, 2015, : 760 - 764
  • [46] Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization
    Li, Rongrong
    Qiu, Linrun
    Zhang, Dongbo
    INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE, 2019, 13 (02) : 18 - 29
  • [47] Microgrid Economic Operation Research Based on Improved Particle Swarm Optimization Algorithm
    Wang, Xueying
    Li, Peng
    PROCEEDINGS OF THE 2015 3RD INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND INFORMATION TECHNOLOGY APPLICATIONS, 2015, 35 : 290 - 294
  • [48] Research of BP neural network based on improved particle swarm optimization algorithm
    School of Mechanical and Information Engineering, China University of Mining and Technology, Beijing, China
    不详
    不详
    J. Netw., 2013, 4 (947-954):
  • [49] Research on Virtual Machine Load Balancing Based on Improved Particle Swarm Optimization
    Li, Wei
    Jian, Tiantian
    Wang, Yanshan
    Ma, Xiang
    2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), 2019, : 2846 - 2852
  • [50] Research on Improved Train Automatic Control Strategy Based on Particle Swarm Optimization
    Li, Weidong
    Li, Xiaoyan
    Liu, Yang
    Hua, Chuntong
    PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 5867 - 5872