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
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