School of Information Engineering, Sanming University, Sanming 365004, Fujian, China.
School of Computer Science and Technology, Hainan University, Haikou 570228, Hainan, China.
Math Biosci Eng. 2021 Aug 24;18(6):7076-7109. doi: 10.3934/mbe.2021352.
This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly, and then representative-based hunting (RH) and opposition-based learning (OBL) strategies are added in the exploration and exploitation phases to effectively improve the diversity of search space and local optima avoidance capability of the algorithm, respectively. To verify the optimization performance and the practicability, the proposed algorithm is comprehensively analyzed on standard and CEC2017 benchmark functions and three engineering design problems. The experimental results show that the proposed IHAOHHO has more superior global search performance and faster convergence speed compared to the basic AO and HHO and selected state-of-the-art meta-heuristic algorithms.
本文提出了一种改进的混合 Aquila 优化器（AO）和哈里斯鹰优化算法（HHO），即 IHAOHHO，以增强全局优化问题的搜索性能。在 IHAOHHO 中，首先保留了 AO 和 HHO 的有价值的探索和开发能力，然后在探索和开发阶段分别添加基于代表的狩猎（RH）和基于对立的学习（OBL）策略，以有效提高搜索空间的多样性和算法的局部最优避免能力。为了验证优化性能和实用性，本文在标准和 CEC2017 基准函数以及三个工程设计问题上对所提出的算法进行了全面分析。实验结果表明，与基本的 AO 和 HHO 以及选定的最先进的启发式算法相比，所提出的 IHAOHHO 具有更好的全局搜索性能和更快的收敛速度。
