Logistics distribution center location using multi-swarm cooperative particle swarm optimizer

被引:3
作者
Tan, Lijing [1 ]
Niu, Ben [2 ]
Lin, Fuyong [1 ]
机构
[1] Management School, Jinan University, Guangzhou
[2] College of Management, Shenzhen University, Shenzhen
基金
中国国家自然科学基金;
关键词
Logistics distribution centers; MCPSO; Particle swarm optimization;
D O I
10.3923/itj.2013.7770.7773
中图分类号
学科分类号
摘要
This study presented a new approach to solve logistics distribution center location problem. Multi-swarm Cooperative Particle Swarm Optimizer (MCPSO) (Niu et al., 2007) is adopted to selects a certain number of locations as distribution centers in a logistics system so as to minimize the total cost of the whole logistics networks. A hybrid parallel encoding method is used and thus logistics distribution center lacation problem is mapped to the process of is birds (particles) foraging. By competition and collaboration of the individuals in MCPSO the optimal lacation solution is obtained. The experimental result demonstrated that the MCPSO achieves rapid convergence rate and better solutions compared with standard PSO. © 2013 Asian Network for Scientific Information.
引用
收藏
页码:7770 / 7773
页数:3
相关论文
共 50 条
  • [1] MCPSO: A multi-swarm cooperative particle swarm optimizer
    Niu, Ben
    Zhu, Yunlong
    He, Xiaoxian
    Wu, Henry
    APPLIED MATHEMATICS AND COMPUTATION, 2007, 185 (02) : 1050 - 1062
  • [2] Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer
    Zhang, Yong
    Gong, Dun-wei
    Ding, Zhong-hai
    EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (11) : 13933 - 13941
  • [3] Dynamic multi-swarm differential learning particle swarm optimizer
    Chen, Yonggang
    Li, Lixiang
    Peng, Haipeng
    Xiao, Jinghua
    Wu, Qingtao
    SWARM AND EVOLUTIONARY COMPUTATION, 2018, 39 : 209 - 221
  • [4] A Multi-Swarm Cooperative Perturbed Particle Swarm Optimization
    Yang, Xiangjun
    Zhao, Yilong
    Chen, Yuchuang
    Zhao, Xinchao
    ADVANCED RESEARCH ON AUTOMATION, COMMUNICATION, ARCHITECTONICS AND MATERIALS, PTS 1 AND 2, 2011, 225-226 (1-2): : 619 - 622
  • [5] Multi-Swarm Particle Swarm Optimizer with Mutation and Its Research in Biomedical Information Classification Optimizer
    Li, Mi
    Chen, Huan
    Zhang, Ming
    Liu, Xingwang
    Lu, Shengfu
    JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2018, 8 (08) : 1619 - 1626
  • [6] Multi-swarm optimizer applied in water distribution networks
    Surco, Douglas F.
    Macowski, Diogo H.
    Coral, Joao G. L.
    Cardoso, Flavia A. R.
    Vecchi, Thelma P. B.
    Ravagnani, Mauro A. S. S.
    DESALINATION AND WATER TREATMENT, 2019, 161 : 1 - 13
  • [7] A Multi-Swarm Self-Adaptive and Cooperative Particle Swarm Optimization
    Zhang, Jiuzhong
    Ding, Xueming
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2011, 24 (06) : 958 - 967
  • [8] Improving the performance of a FBG sensor network using a novel dynamic multi-swarm particle swarm optimizer
    Liang, J. J.
    Chan, C. C.
    Huang, V. L.
    Suganthan, N.
    OPTOELECTRONICS AND ADVANCED MATERIALS-RAPID COMMUNICATIONS, 2007, 1 (08): : 373 - 378
  • [9] Dynamic Multi-swarm Particle Swarm Optimization with Center Learning Strategy
    Zhu, Zijian
    Zhong, Tian
    Wu, Chenhan
    Xue, Bowen
    ADVANCES IN SWARM INTELLIGENCE, ICSI 2022, PT I, 2022, : 141 - 147
  • [10] An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization
    Kong, Fanrong
    Jiang, Jianhui
    Huang, Yan
    MATHEMATICS, 2019, 7 (06)