A hybrid ITLHHO algorithm for numerical and engineering optimization problems

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.

Automated summary

Harris Hawks Optimization (HHO) is a swarm optimization technique that mimics the behavior of Harris hawks, but it may get trapped in local search space. An improved hybrid method, ITLHHO, has been developed to significantly outperform other algorithms and become a promising tool for various optimization problems.