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AFSA-SLnO Variants for Enhanced Global Optimization

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Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 829))

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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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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Pulau Pinang, Malaysia

    Norazian Subari & Junita Mohamad-Saleh

  2. Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan Campus, 26600, Pekan, Pahang, Malaysia

    Norazian Subari & Noorazliza Sulaiman

Authors
  1. Norazian Subari
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  2. Junita Mohamad-Saleh
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  3. Noorazliza Sulaiman
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Corresponding author

Correspondence to Junita Mohamad-Saleh .

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia

    Nor Muzlifah Mahyuddin

  2. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia

    Nor Rizuan Mat Noor

  3. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia

    Harsa Amylia Mat Sakim

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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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