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Integrating mutation scheme into monarch butterfly algorithm for global numerical optimization

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Abstract

Monarch butterfly optimization algorithm (MBO) has recently been proposed as a robust metaheuristic optimization algorithm for solving numerical global optimization problems. To enhance the performance of MBO algorithm, harmony search (HS) is introduced as a mutation operator during the adjusting operator of MBO. A novel hybrid metaheuristic optimization method, the so-called HMBO, is introduced to find the best solution for the global optimization problems. HMBO combines HS exploration with MBO exploitation, and therefore, it produces potential candidate solutions. The implementation process for enhancing MBO method is also presented. To evaluate the effectiveness of this improvement, fourteen standard benchmark functions are used. The mean and the best performance of these benchmark functions in 20, 50, and 100 dimensions demonstrated that HMBO often performs better than the original MBO and other population-based optimization algorithms such as ACO, BBO, DE, ES, GAPBIL, PSO and SGA. Moreover, the t-test result proved that the performance differences between the enhanced HMBO and the original MBO as well as the other optimization methods are statistically significant.

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  1. School of Computer Science, Universiti Sains Malaysia USM, 11800, Kota Bharu, Pulau Pinang, Malaysia

    Mohamed Ghetas & Huah Yong Chan

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  1. Mohamed Ghetas
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  2. Huah Yong Chan
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Correspondence to Mohamed Ghetas.

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Ghetas, M., Chan, H.Y. Integrating mutation scheme into monarch butterfly algorithm for global numerical optimization. Neural Comput & Applic 32, 2165–2181 (2020). https://doi.org/10.1007/s00521-018-3676-x

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