Harris hawks optimization (HHO) is one of the newest metaheuristic algorithms (MHAs) which mimic the interdependent behaviour and hunting style of Harris hawks in nature. It is an efficient swarm optimization technique that has been used to solve various kinds of optimization problems. However, for some optimization cases, it has a tendency to be trapped into local search space and it endures an improper balance between exploitation and exploration. To get rid of this situation and to explore the global searching ability of HHO, an effective hybrid method improved teaching–learning HHO (ITLHHO) has been developed using improved teaching–learning-based optimization for solving different kinds of engineering design and numerical optimization problems. The performance of ITLHHO has been demonstrated by 33 well-known benchmark functions, including IEEE Congress of Evolutionary Computation Benchmark Test Functions (CEC-C06, 2019 Competition) and 10 multidisciplinary challenging engineering optimization problems. After illustration, the outcomes of the proposed ITLHHO are compared with several recently developed competitive MHAs. Additionally, the ITLHHO results are statistically investigated with the Wilcoxon rank-sum test and multiple comparison test to show the significance of the results. The experimental results suggest that ITLHHO significantly outperforms other algorithms and becomes a remarkable and promising tool for solving various kinds of optimization problems.

中文翻译：

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一种用于数值和工程优化问题的混合 ITLHHO 算法

哈里斯鹰优化 (HHO) 是最新的元启发式算法 (MHA) 之一，它模仿自然界中哈里斯鹰的相互依存行为和狩猎方式。它是一种有效的群体优化技术，已被用于解决各种优化问题。然而，对于某些优化案例，它有陷入局部搜索空间的趋势，并且在开发和探索之间存在不适当的平衡。为了摆脱这种情况并探索HHO的全局搜索能力，开发了一种有效的混合方法改进教学HHO（ITLHHO），使用改进的基于教学的优化来解决不同类型的工程设计和数值优化问题. ITLHHO 的性能已通过 33 个著名的基准函数得到证明，包括 IEEE 进化计算基准测试函数大会（CEC-C06，2019 竞赛）和 10 个多学科具有挑战性的工程优化问题。在说明之后，将提议的 ITLHHO 的结果与最近开发的几个竞争性 MHA 进行比较。此外，使用 Wilcoxon 秩和检验和多重比较检验对 ITLHHO 结果进行统计研究，以显示结果的显着性。实验结果表明，ITLHHO 显着优于其他算法，成为解决各种优化问题的显着且有前途的工具。提议的 ITLHHO 的结果与最近开发的几个竞争性 MHA 进行了比较。此外，使用 Wilcoxon 秩和检验和多重比较检验对 ITLHHO 结果进行统计研究，以显示结果的显着性。实验结果表明，ITLHHO 显着优于其他算法，成为解决各种优化问题的显着且有前途的工具。提议的 ITLHHO 的结果与最近开发的几个竞争性 MHA 进行了比较。此外，使用 Wilcoxon 秩和检验和多重比较检验对 ITLHHO 结果进行统计研究，以显示结果的显着性。实验结果表明，ITLHHO 显着优于其他算法，成为解决各种优化问题的显着且有前途的工具。