Spotted hyena optimization (SHO) is one of the newly evolved swarm-based metaheuristic optimization methods based on the social life cycle of hyenas. In recent times SHO is being applied to various engineering applications as well as to solve real-life complications. In this paper, we have hybridized SHO with quadratic approximation operator (QAO), termed as QASHO. The proposed QASHO has been scrutinized to enhance the exploitation ability, aiming to achieve global optimum, as QAO performs better within the local confinement region. Furthermore, the proposed approach shows improved strength in terms of escaping from the local minima trap, as in each iteration we discard some of the worst individuals by some suitable ones. To validate the proficiency of the proposed QASHO approach, 28 standard problems have been preferred in connection with IEEE-CEC-2017. The outcome observed from the suggested method has also equated with contemporary metaheuristic approaches. To prove the statistical significance, a nonparametric test has also been accomplished. Additionally as a real-life application, the suggested approach QASHO has utilized to train wavelet higher-order neural networks (HONN) by choosing datasets from the UCI store. The above correlations reveal that QASHO can deal with complex optimization tasks.
Keywords: Global optimization; Metaheuristic approach; Quadratic approximation (QA); Spotted hyena optimizer (SHO); Swarm-based algorithm; Wavelet neural network.
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