An Artificial Bee Colony Algorithm based Optimization Method for Service Network Customization

被引:2
|
作者
Wang, Shaopeng [1 ]
Wang, Zhongjie [1 ]
Xu, Xiaofei [1 ]
机构
[1] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150006, Peoples R China
关键词
Service network; personalized requirements; mass customization; Artificial Bee Colony (ABC);
D O I
10.1109/ICSS.2013.9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents an artificial bee colony (ABC) algorithm based method for customizing an existing service network (SN). Service network is a complex network composed of a set of service components and the input-output relationships between them, and it has the powerful capacity of being customized to fulfill different personalized customer requirements. There might be multiple customized solutions in terms of one requirement, and different solution has varied usage cost, therefore to find the optimal one with minimal cost is necessary. Because it is a NP-hard problem, we propose an optimization method based on ABC algorithm, where a food source represents one solution (a sub network), and the optimization goal is to minimize the cost under the constraints of total response time and reliability raised by the customer. The optimal solution is found based on the group intelligence of bees. Experiment results demonstrate the ABC algorithm can quickly find the optimal customized solution.
引用
收藏
页码:101 / 106
页数:6
相关论文
共 50 条
  • [1] Optimization method for cloud manufacturing service composition based on the improved artificial bee colony algorithm
    Hu, Qiang
    Tian, Yuqing
    Qi, Haoquan
    Wu, Peng
    Liu, Qingxue
    [J]. Tongxin Xuebao/Journal on Communications, 2023, 44 (01): : 200 - 210
  • [2] Web service composition optimization based on improved artificial bee colony algorithm
    He, Jun
    Chen, Liang
    Wang, Xiaolong
    Li, Yonggang
    [J]. Journal of Networks, 2013, 8 (09) : 2143 - 2149
  • [3] Urban Road Network Optimization Based on Improved Artificial Bee Colony Algorithm
    Luo Jie
    Lu Baichuan
    Hong Jin
    [J]. ICVISP 2019: PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON VISION, IMAGE AND SIGNAL PROCESSING, 2019,
  • [4] Clustering Algorithm Based on Artificial Bee Colony Optimization
    Zhang, Dandan
    Luo, Ke
    [J]. PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND ENGINEERING INNOVATION, 2015, 12 : 126 - 131
  • [5] Parallel Optimization Based on Artificial Bee Colony Algorithm
    Li, Debo
    Feng, Yongxin
    Zhong, Jun
    Zhou, Jielian
    Yin, Libao
    Zhou, Junhao
    [J]. 2017 IEEE 2ND INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA), 2017, : 955 - 959
  • [6] Improved Artificial Bee Colony Algorithm for Multimodal Optimization Based on Crowding Method
    Ma, Shijing
    Wang, Yunhe
    Zhang, Shouwei
    [J]. JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING, 2022, 34 (03)
  • [7] Web Service Composition Optimization Method Based on Improved Multi-objective Artificial Bee Colony Algorithm
    Song, Hang
    Wang, Ya-Li
    Liu, Guo-Qi
    Zhang, Bin
    [J]. Dongbei Daxue Xuebao/Journal of Northeastern University, 2019, 40 (06): : 777 - 782
  • [8] An Artificial Bee Colony Optimization Algorithm Guided by Complex Method
    He, Dengxu
    Jia, Ruimin
    Shi, Shaotang
    [J]. 2012 FIFTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2012), VOL 1, 2012, : 348 - 351
  • [9] Miner revenue optimization algorithm based on Pareto artificial bee colony in blockchain network
    Yourong Chen
    Hao Chen
    Meng Han
    Banteng Liu
    Qiuxia Chen
    Zhenghua Ma
    Zhangquan Wang
    [J]. EURASIP Journal on Wireless Communications and Networking, 2021
  • [10] Trusted Network Difference Data Mining Algorithm Based on Artificial Bee Colony Optimization
    Li, Junmei
    Chen, Huafeng
    Li, Suruo
    [J]. Journal of Testing and Evaluation, 2022, 51 (03):