Abstract
This paper introduces hybridized elephant herding optimization algorithm (EHO) adopted for solving constrained optimization problems. EHO is one of the latest swarm intelligence metaheuristic and the implementation of the EHO for constrained optimization was not found in literature. In order to evaluate the performance of the hybridized EHO algorithm, we conducted tests on 13 standard constrained benchmark functions. To prove efficiency and robustness of the hybridized EHO, a comparative analysis with basic EHO implementation, as well as with other state-of-the-art algorithms, such as firefly algorithm, seeker optimization algorithm and self-adaptive penalty function genetic algorithm was performed. Experiments show that the hybridized EHO on average outperforms other algorithms used in comparative analysis.
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Acknowledgment
This research is supported by Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.
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Strumberger, I., Bacanin, N., Tuba, M. (2018). Hybridized Elephant Herding Optimization Algorithm for Constrained Optimization. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_16
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DOI: https://doi.org/10.1007/978-3-319-76351-4_16
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