Parameters optimization of cognitive network based on artificial physics multi-objective algorithm

被引：0
|
作者：
Chai, Zheng-Yi [1 ,3 ]
Wang, Bing [2 ]
Li, Ya-Lun [1 ]
Zhu, Si-Feng [4 ]
Wang, Ying-Feng [5 ]
机构：
[1] School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin,300384, China
[2] Department of Maritime, Henan Vocational and Technical College of Communications, Zhengzhou,Henan,450005, China
[3] Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing,100876, China
[4] School of Mathematics and Statistics, Zhoukou Normal University, Zhoukou,Henan,466001, China
[5] College of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou,Henan,450000, China
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2015年 / 43卷 / 08期
关键词：
Pareto principle - Parameter estimation - Cognitive radio - Engines - Hamming distance;
D O I：
10.3969/j.issn.0372-2112.2015.08.009
中图分类号：
学科分类号：
摘要：
To solve the engine parameter adjustment problem of cognitive radio networks, an artificial physics multi-objective optimization algorithm was proposed. According to its binary encoded features of cognitive parameters, Hamming distance based individual ranking method was designed and particle updated equation was improved, and finally the Pareto optimal set were achieved. Simulation results show that under the multi-carrier environment, the proposed algorithm can adjust transmission power and modulation mode according to the changing of channel and cognitive user demands. So it meets the demands for parameters optimization. ©, 2015, Chinese Institute of Electronics. All right reserved.
引用
收藏
页码：1526 / 1530
相关论文
共 50 条
  • [1] Multi-objective optimization algorithm based on artificial physics optimization
    Wang, Yan
    Zeng, Jian-Chao
    Kongzhi yu Juece/Control and Decision, 2010, 25 (07): : 1040 - 1044
  • [2] A multi-objective artificial physics optimization algorithm based on ranks of individuals
    Yan Wang
    Jian-chao Zeng
    Soft Computing, 2013, 17 : 939 - 952
  • [3] A multi-objective artificial physics optimization algorithm based on ranks of individuals
    Wang, Yan
    Zeng, Jian-chao
    SOFT COMPUTING, 2013, 17 (06) : 939 - 952
  • [4] An Improved Multi-Objective Artificial Physics Optimization Algorithm Based on Multi-Strategy Fusion
    Sun, Bao
    Zhang, Lijing
    Li, Zhanlong
    Fan, Kai
    Jin, Qinqin
    Guo, Jin
    IEEE ACCESS, 2022, 10 : 108736 - 108748
  • [5] Multi-objective optimization of laser perforated fuel filter parameters based on artificial neural network and genetic algorithm
    Wang, Yifan
    Zhang, Tianyi
    Chen, Lei
    Tao, Wenquan
    PARTICUOLOGY, 2025, 96 : 57 - 70
  • [6] Multi-objective optimization of spectrum detection in cognitive IoT using artificial physics
    Li, Yalun
    Wang, Honghai
    Chai, Zhengyi
    JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS, 2019, 42 (03) : 219 - 224
  • [7] An Artificial Physics Optimization Algorithm for Multi-Objective Problems Based on Virtual Force Sorting Proceedings
    Wang, Yan
    Zeng, Jian-chao
    Tan, Ying
    SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, 2010, 6466 : 615 - +
  • [8] An Artificial Immune Network for Multi-objective Optimization
    Lanaridis, Aris
    Stafylopatis, Andreas
    ARTIFICIAL NEURAL NETWORKS-ICANN 2010, PT II, 2010, 6353 : 531 - 536
  • [9] A novel multi-objective optimization algorithm based on artificial algae for multi-objective engineering design problems
    Mohamed A. Tawhid
    Vimal Savsani
    Applied Intelligence, 2018, 48 : 3762 - 3781
  • [10] A novel multi-objective optimization algorithm based on artificial algae for multi-objective engineering design problems
    Tawhid, Mohamed A.
    Savsani, Vimal
    APPLIED INTELLIGENCE, 2018, 48 (10) : 3762 - 3781
12345