Pareto fronts-driven Multi-Objective Cuckoo Search for 5G Network Optimization

被引:2
作者
Wang, Junyan [1 ]
机构
[1] Taiyuan Univ Sci & Technol, Sch Comp Sci & Technol, Taiyuan, Shanxi, Peoples R China
来源
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | 2020年 / 14卷 / 07期
基金
中国国家自然科学基金;
关键词
Cuckoo search; Pareto fronts; Convergence; Diversity; 5G Network Optimization; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM; DESIGN; SYSTEMS; VERSION; ENERGY;
D O I
10.3837/tiis.2020.07.004
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
5G network optimization problem is a challenging optimization problem in the practical engineering applications. In this paper, to tackle this issue, Pareto fronts-driven Multi-Objective Cuckoo Search (PMOCS) is proposed based on Cuckoo Search. Firstly, the original global search manner is upgraded to a new form, which is aimed to strengthening the convergence. Then, the original local search manner is modified to highlight the diversity. To test the overall performance of PMOCS, PMOCS is test on three test suits against several classical comparison methods. Experimental results demonstrate that PMOCS exhibits outstanding performance. Further experiments on the 5G network optimization problem indicates that PMOCS is promising compared with other methods.
引用
收藏
页码:2800 / 2814
页数:15
相关论文
共 50 条
  • [41] Multi-objective optimization based on aspiration levels and approximation of Pareto frontier
    Yun, Yeboon
    Nakayama, Hirotaka
    Yoon, Min
    CJK-OSM 4: The Fourth China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems, 2006, : 491 - 496
  • [42] Multi-objective optimization sampling based on Pareto optimality for soil mapping
    Li, Xiaolan
    Gao, Bingbo
    Pan, Yuchun
    Bai, Zhongke
    Gao, Yunbing
    Dong, Shiwei
    Li, Shuhua
    GEODERMA, 2022, 425
  • [43] DEEP CONVOLUTIONAL NEURAL NETWORKS FOR PARETO OPTIMAL FRONT OF MULTI-OBJECTIVE OPTIMIZATION PROBLEM
    Liu, Ruilin
    Zhang, Tao
    Chen, Fang
    JOURNAL OF NONLINEAR AND CONVEX ANALYSIS, 2022, 23 (04) : 833 - 846
  • [44] Pareto-based multi-objective optimization for classification in data mining
    Kamila, Narendra Kumar
    Jena, Lambodar
    Bhuyan, Hemanta Kumar
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2016, 19 (04): : 1723 - 1745
  • [45] Multi-objective Pareto and GAs nonlinear optimization approach for flyback transformer
    Barg, Sobhi
    Bertilsson, Kent
    ELECTRICAL ENGINEERING, 2019, 101 (03) : 995 - 1006
  • [46] A quantum multi-objective optimization algorithm based on harmony search method
    Sadeghi Hesar, Alireza
    Kamel, Seyed Reza
    Houshmand, Mahboobeh
    SOFT COMPUTING, 2021, 25 (14) : 9427 - 9439
  • [47] Pareto local search algorithms for the multi-objective beam angle optimisation problem
    Cabrera-Guerrero, Guillermo
    Mason, Andrew J.
    Raith, Andrea
    Ehrgott, Matthias
    JOURNAL OF HEURISTICS, 2018, 24 (02) : 205 - 238
  • [48] Zigzag search for multi-objective optimization considering generation cost and emission
    Zhang, Qiwei
    Li, Fangxing
    Wang, Honggang
    Xue, Yaosuo
    APPLIED ENERGY, 2019, 255
  • [49] A Nonlinear Simplex Search Approach for Multi-Objective Optimization
    Zapotecas Martinez, Saul
    Arias Montano, Alfredo
    Coello Coello, Carlos A.
    2011 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2011, : 2367 - 2374
  • [50] A new mechanism for maintaining diversity of Pareto archive in multi-objective optimization
    Hajek, Jaroslav
    Szollos, Andras
    Sistek, Jakub
    ADVANCES IN ENGINEERING SOFTWARE, 2010, 41 (7-8) : 1031 - 1057