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In this article, the exploration and stochastic property of elite opposition‐based learning and chaotic maps are utilized to introduce a hybridized metaheuristic optimization technique. Both the techniques are combined with state of matter search optimization to enhance its capability of locating global minima. The proposed hybrid algorithm is tested on various benchmark functions and compared with the state of mater search optimization to verify its efficiency. The results show that the proposed hybrid algorithm gives better convergence for various benchmark functions.

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