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Metaheuristic algorithms that are used for solving NP-hard optimization problems include two main exploitation and exploration components, and try to disturb a balance between exploitation and exploration to achieve the best possible near-optimal solution. One of the metaheuristic algorithms is the bat algorithm with poor exploration and exploitation. In this paper, the exploration and exploitation processes of Gray Wolf Optimizer (GWO) algorithm are applied to some of the solutions produced by<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3774833">doi:10.5281/zenodo.3774833</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ow6noalktzdsxox3xuw2nx4v7a">fatcat:ow6noalktzdsxox3xuw2nx4v7a</a> </span>
more »... the bat algorithm. Therefore, part of the population of the bat algorithm is changed by two processes (i.e. exploration and exploitation) of GWO; the new population enters the bat algorithm population when its result is better than that of the exploitation and exploration operators of the bat algorithm. Thereby, better new solutions are introduced into the bat algorithm at each step. In this paper, using 20 mathematic benchmark functions, the proposed method was evaluated and compared. The simulation outcomes indicate that in most implementations the proposed method outperforms the bat algorithm and other metaheuristic algorithms and has a high performance.
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