Abstract
Artificial fish swarm algorithm (AFSA) is a strategy which imitates the natural behavior of fish swarm in the real environment. Many improvements and modifications have been proposed on AFSA to improve its optimization performance. To date, nevertheless, the existing algorithms are still unable to achieve a satisfactory global optimum. This paper presents incorporation of circle updating position from Sea Lion Optimization (SLnO) into AFSA to enhance the robustness and optimum value. Fifteen benchmarks function have been used to evaluate the performance of the proposed variants in comparison to the standard AFSA and SLnO. The proposed variants show better result compared to the standard AFSA and SLnO.
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Acknowledgements
This work has been supported by Ministry of Higher Education (MOHE) Malaysia Fundamental Research Grant Scheme (Grant No: RDU190180) and (MOHE) Malaysia Fundamental Research Grant Scheme (Grant No: 203.PELECT.6071371).
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Subari, N., Mohamad-Saleh, J., Sulaiman, N. (2022). AFSA-SLnO Variants for Enhanced Global Optimization. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_79
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DOI: https://doi.org/10.1007/978-981-16-8129-5_79
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