AOAAO: The Hybrid Algorithm of Arithmetic Optimization Algorithm With Aquila Optimizer

被引:46
|
作者
Zhang, Yu-Jun [1 ]
Yan, Yu-Xin [2 ]
Zhao, Juan [1 ]
Gao, Zheng-Ming [3 ,4 ]
机构
[1] Jingchu Univ Technol, Sch Elect & Informat Engn, Jingmen 448000, Peoples R China
[2] Jingchu Univ Technol, Sch Arts, Jingmen 448000, Peoples R China
[3] Jingchu Univ Technol, Sch Comp Engn, Jingmen 448000, Peoples R China
[4] Hubei Jingmen Ind Technol Res Inst, Inst Intelligent Informat Technol, Jingmen 448000, Peoples R China
关键词
Optimization; Arithmetic; Classification algorithms; Convergence; Mathematical models; Genetic algorithms; Search problems; Aquila optimizer (AO); arithmetic optimization algorithm (AOA); piecewise linear map; hybrid algorithm; HEURISTIC OPTIMIZATION; COLONY;
D O I
10.1109/ACCESS.2022.3144431
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Many new algorithms have been proposed to solve the mathematical equations formulated to describe the real-world problems. But there still does not exist one algorithm that could solve the problems all. And most of the proposed algorithms have defects in some aspects, they need to be improved in application. In order to find a more efficient optimization algorithm and inspired by the better performance of the Arithmetic Optimization algorithm (AOA) and Aquila Optimizer (AO), we proposed a hybridization algorithm of them and abbreviated AOAAO in this paper. Considering the better performance of the Harris Hawk optimization (HHO) algorithm, an energy parameter E was also introduced to balance the exploration and exploitation procedures of individuals in AOAAO swarms, and furthermore, piecewise linear map was introduced to decrease the randomness of the energy parameter. Pseudo code of the proposed AOAAO algorithm was presented, Simulation experiments were carried out on the benchmark functions and three classical engineering problems were also involved in optimization. Nine popular well demonstrated algorithms were included for comparison. Results confirmed the AOAAO would be more efficient in optimization with faster convergence rate, and higher convergence accuracy.
引用
收藏
页码:10907 / 10933
页数:27
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