4.6 Article

Accelerated Arithmetic Optimization Algorithm by Cuckoo Search for Solving Engineering Design Problems

期刊

PROCESSES
卷 11, 期 5, 页码 -

出版社

MDPI
DOI: 10.3390/pr11051380

关键词

machine learning; AOA; cuckoo search; welded beam; Truss bar

向作者/读者索取更多资源

This paper proposes an improved arithmetic optimization algorithm (AOA) called AOACS based on the cuckoo search algorithm to solve engineering optimization problems. AOACS uses cuckoo search algorithm operators to enhance the exploitation operations of AOA and improve convergence ratio. The performance of AOACS is evaluated using benchmark functions and engineering design problems, and compared to state-of-the-art approaches, demonstrating its superior performance.
Several metaheuristic algorithms have been implemented to solve global optimization issues. Nevertheless, these approaches require more enhancement to strike a suitable harmony between exploration and exploitation. Consequently, this paper proposes improving the arithmetic optimization algorithm (AOA) to solve engineering optimization issues based on the cuckoo search algorithm called AOACS. The developed approach uses cuckoo search algorithm operators to improve the ability of the exploitation operations of AOA. AOACS enhances the convergence ratio of the presented technique to find the optimum solution. The performance of the AOACS is examined using 23 benchmark functions and CEC-2019 functions to show the ability of the proposed work to solve different numerical optimization problems. The proposed AOACS is evaluated using four engineering design problems: the welded beam, the three-bar truss, the stepped cantilever beam, and the speed reducer design. Finally, the results of the proposed approach are compared with state-of-the-art approaches to prove the performance of the proposed AOACS approach. The results illustrated an outperformance of AOACS compared to other methods of performance measurement.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据