用于全局优化和约束工程问题的增强型天鹰座优化算法。

Enhanced Aquila optimizer algorithm for global optimization and constrained engineering problems.

作者信息

Yu Huangjing, Jia Heming, Zhou Jianping, Hussien Abdelazim G

机构信息

School of Information Engineering, Sanming University, Sanming 365004, China.

Department of Computer and Information Science, Linköping University, 581 83 Linköping, Sweden.

出版信息

Math Biosci Eng. 2022 Sep 27;19(12):14173-14211. doi: 10.3934/mbe.2022660.

DOI:10.3934/mbe.2022660

PMID:36654085

Abstract

The Aquila optimizer (AO) is a recently developed swarm algorithm that simulates the hunting behavior of Aquila birds. In complex optimization problems, an AO may have slow convergence or fall in sub-optimal regions, especially in high complex ones. This paper tries to overcome these problems by using three different strategies: restart strategy, opposition-based learning and chaotic local search. The developed algorithm named as mAO was tested using 29 CEC 2017 functions and five different engineering constrained problems. The results prove the superiority and efficiency of mAO in solving many optimization issues.

摘要

天鹰座优化器（AO）是一种最近开发的群体算法，它模拟了天鹰座鸟类的狩猎行为。在复杂的优化问题中，AO可能收敛速度慢或陷入次优区域，尤其是在高度复杂的问题中。本文试图通过使用三种不同的策略来克服这些问题：重启策略、基于反向学习和混沌局部搜索。所开发的名为mAO的算法使用29个CEC 2017函数和五个不同的工程约束问题进行了测试。结果证明了mAO在解决许多优化问题方面的优越性和有效性。