Recently established Harris hawks optimization (HHO) has natural behavior for finding an optimum solution in global search space without getting trapped in previous convergence. However, the exploitation phase of the current Harris hawks optimizer algorithm is poor. In the present research, an improved version of the HHO algorithm, which combines Harris hawks optimizer with Canis lupus inspire grey wolf optimizer (GWO) and named as hHHO‐GWO algorithm, has been proposed to find the solution of various optimization problems such as nonlinear, nonconvex, and highly constrained engineering design problem. In the proposed research, the phase of exploration and exploitation of the existing HHO algorithm has been further improved using GWO algorithm and its performance has been tested for various benchmarks problems including CEC2005 (unimodal, multimodal, and fixed dimensions functions), multimodal functions with variable dimensions, and CEC‐BC‐2017 test functions. Further, the developed hybrid optimizer has been tested for 11 different engineering design and optimization problems and experimental results of hHHO‐GWO have been compared with other optimizer.

中文翻译：

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以Canis狼疮为灵感的升级版Harris鹰式优化器，可解决非线性，受限，连续和离散工程设计问题

最近建立的哈里斯鹰优化（HHO）具有自然行为，可以在全局搜索空间中找到最佳解决方案，而不会陷入先前的收敛中。但是，当前的Harris hawks优化器算法的开发阶段很差。在本研究中，HHO算法的改进版本将Harris鹰优化器与Canis狼疮相结合提出了一种启发式灰狼优化器（GWO），称为hHHO-GWO算法，以找到各种优化问题的解决方案，例如非线性，非凸性和高度受限的工程设计问题。在拟议的研究中，使用GWO算法进一步改善了现有HHO算法的探索和开发阶段，并针对各种基准问题（包括CEC2005（单峰，多峰和固定维函数），可变多峰函数）测试了其性能。尺寸和CEC‐BC‐2017测试功能。此外，已开发的混合优化器针对11种不同的工程设计和优化问题进行了测试，并将hHHO-GWO的实验结果与其他优化器进行了比较。