Abstract
The most recent study trend is to combine two or more variations to improve the quality of solutions to practical and contemporary real-world global optimization challenges. In this work, a novel Sine Cosine Algorithm (SCA) and hybrid Grey Wolf Optimization (GWO) technique is tested on 10 benchmark tests. A hybrid GWOSCA is a mixture of the Sine Cosine Algorithm (SCA) for the exploration phase and the Grey Wolf Optimizer (GWO) for the exploitation phase in an undefined environment. The simulation findings reveal that the suggested hybrid technique outperforms, better than other known algorithms in the research community.
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Sarangi, P., Mohapatra, P. (2023). Modified Hybrid GWO-SCA Algorithm for Solving Optimization Problems. In: Yadav, A., Gupta, G., Rana, P., Kim, J.H. (eds) Proceedings on International Conference on Data Analytics and Computing. ICDAC 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 175. Springer, Singapore. https://doi.org/10.1007/978-981-99-3432-4_10
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DOI: https://doi.org/10.1007/978-981-99-3432-4_10
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