Self-organizing hierarchical mayfly optimization algorithm

被引：0
|
作者：
Gao, Zheng-Ming [1 ]
Li, Su-Ruo [1 ]
Zhao, Juan [2 ]
Hu, Yu-Rong [3 ]
机构：
[1] Jingchu Univ technol, Sch Comp Engn, Jingmen, Peoples R China
[2] Jingchu Univ technol, Sch Elect & informat Engn, Jingmen, Peoples R China
[3] Jingchu Univ technol, Dept Sci & technol, Jingmen, Peoples R China
来源：
2020 INTERNATIONAL CONFERENCE ON BIG DATA & ARTIFICIAL INTELLIGENCE & SOFTWARE ENGINEERING (ICBASE 2020) | 2020年
关键词：
mayfly optimization algorithm; benchmark function; simulation experiment; Monte Carlo method; PARTICLE SWARM;
D O I：
10.1109/ICBASE51474.2020.00081
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the self-organizing hierarchical method was introduced to improve the capability of optimization for the newly proposed mayfly optimization (MO) algorithm. The weighted current velocities were removed from the updating equations and the re-initialization would be carried out if the updated velocities were turned to zero. Simulation experiments were carried out with the Monte Carlo method. Results verified the better performance of the improved MO algorithm than before.
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页码：355 / 358
页数：4
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