Abstract
This paper presents two improved versions of the monarch butterfly optimization algorithm adopted for solving multi-objective optimization problems. Monarch butterfly optimization is a relatively new swarm intelligence metaheuristic that proved to be robust and efficient method when dealing with NP hard problems. However, in the original monarch butterfly approach some deficiencies were noticed and we addressed these deficiencies by developing one modified, and one hybridized version of the original monarch butterfly algorithm. In the experimental section of this paper we show comparative analysis between the original, and improved versions of monarch butterfly algorithm. According to experimental results, hybridized monarch butterfly approach outperformed all other metaheuristics included in comparative analysis.
This research is supported by Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.
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This research is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.
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Strumberger, I., Tuba, E., Bacanin, N., Beko, M., Tuba, M. (2020). Modified and Hybridized Monarch Butterfly Algorithms for Multi-Objective Optimization. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_44
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