This work presents the region-based quantum evolutionary algorithm (RQEA) for solving numerical optimization problems. In the proposed algorithm, the feasible solution space is decomposed into regions in terms of quantum representation. As the search progresses from one generation to the next, the quantum bits evolve gradually, increasing the probability of selecting regions that yield good fitness values. Through the inherent probabilistic mechanism, the RQEA initially behaves as a global search algorithm and gradually evolves into a local search algorithm, resulting in a good balance between exploration and exploitation. The RQEA is applied to a series of numerical optimization problems. The experiments show that the results obtained by the RQEA are better than those obtained using state-of-the-art QEA and DEahcSPX.
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