Boosted Harris Hawks gravitational force algorithm for global optimization and industrial engineering problems

Harris Hawks Optimization (HHO) is a newly proposed metaheuristic algorithm, which primarily works based on the cooperative system and chasing behavior of Harris' hawks. In this paper, an augmented modification called HHMV is proposed to alleviate the main shortcomings of the conventional HHO that converges tardily and slowly to the optimal solution. Further, it is easy to trap in the local optimum when solving multi-dimensional optimization problems. In the proposed method, the conventional HHO is hybridized with Multi-verse Optimizer to improve its convergence speed, the exploratory searching mechanism through the beginning steps, and the exploitative searching mechanism in the final steps. The effectiveness of the proposed HHMV is deeply analyzed and investigated by using classical and CEC2019 benchmark functions with several dimensions size. Moreover, to prove the ability of the proposed HHMV method in solving real-world problems, five engineering design problems are tested. The experimental results confirmed that the exploration and exploitation search mechanisms of conventional HHO and its convergence speed have been significantly augmented. The HHMV method proposed in this paper is a promising version of HHO, and it obtained better results compared to other state-of-the-art methods published in the literature.

Automated summary

This paper presents a newly proposed metaheuristic algorithm, Harris Hawks Optimization (HHO), and its augmented modification called HHMV. By hybridizing with Multi-verse Optimizer, HHMV improves the convergence speed and search mechanisms of conventional HHO in multi-dimensional optimization problems. Experimental results show that HHMV outperforms other methods in terms of exploration and exploitation search mechanisms and convergence speed.