A self-adaptive Harris Hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection

Harris Hawks Optimization is a recently proposed algorithm inspired by the cooperative manner and chasing behavior of harris. However, from the experimental results, it can be noticed that HHO may fall in local optima or have a slow convergence curve in some complex optimization tasks. In this paper, an improved version of HHO called IHHO is proposed which enhances the performance of HHO by combining HHO with opposition-based learning (OBL), Chaotic Local Search (CLS), and a self-adaptive technique. In order to show the performance of the proposed algorithm, several experiments are conducted using the Standard IEEE CEC 2017 benchmark. IHHO is compared with the classical HHO and other 10 state-of-art algorithms. Moreover, IHHO is used to solve 5 constrained engineering problems. IHHO has also been applied to solve feature selection problem using 7 UCI dataset. The numerical results and analysis show the superiority of IHHO in solving real-world problems.

Automated summary

An improved version of HHO algorithm called IHHO is proposed in this paper, which enhances the performance of HHO by combining OBL, CLS, and self-adaptive technique. Experimental results show the superiority of IHHO in solving complex optimization problems and real-world problems.