Opposition-based learning Harris hawks optimization with advanced transition rules: principles and analysis

Harris hawks optimizer (HHO) is a recently developed, efficient meta-heuristic optimization approach, which is inspired by the chasing style and collaborative behavior of Harris hawks in nature. However, for some optimization cases, the algorithm suffers from an immature balance between exploitation and exploration. Therefore, in the present study, four effective strategies are introduced into conventional HHO, such as proposing a non-linear energy parameter for the nergy of prey, differor rapid dives, a greedy selection mechanism, and opposition-based learning. These strategies enhance the search-efficiency of HHO and help to alleviate the issues of stagnation at the sub-optimal solution and premature convergence. A well-known collection of 33 benchmark problems is taken to examine the effectiveness of the proposed m-HHO, and the comparison is performed with conventional HHO and other state-of-the-art algorithms. Accordingly, the proposed m-HHO can serve as an effective and efficient optimization tool for global optimization problems. (c) 2020 Published by Elsevier Ltd.