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Abstract Maintaining convergence and diversification in solving optimization is one of the most important challenges facing metaheuristic algorithms in general and the bat algorithm in particular. Many researchers have suggested some improvements to preserve the ability of the algorithm to find good solutions in a timely manner and also to move away as much as possible from landing on the local optimization zone. In this paper, a hyper-heuristic method was proposed to incorporate the behavior of three optimized algorithms from the bat algorithm. The method is based on the distribution of a specific implementation probability for each used algorithm and then updating this probability iteratively according to the results of each algorithm, and then we use random selection to determine the algorithm used in the current iteration. Some nonlinear models proposed in CEC2005 used to compare the efficiency of the proposed algorithm and compare its results with some state-of-the-art algorithms.
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