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Triangular mutation-based manta-ray foraging optimization and orthogonal learning for global optimization and engineering problems

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Abstract

Trapping in local solutions is the main issue in several metaheuristic techniques. To solve such drawbacks by enhancing the search agents, a modified search strategy becomes a more attractive tactic. In this paper, an innovative version of Manta Ray Foraging Optimization (MRFO) is proposed to solve its crucial drawbacks while handling global and engineering optimization problems. The proposed version presents an integrated variant of MRFO with the triangular mutation operator and orthogonal learning strategy, called MRTMO. The two approaches are considered to achieve a robust equipoise between algorithm cores and provide a reliable mechanism to guide the search agents during the optimization process. The proposed MRTMO was tested with challenging CEC2005 and CEC2017 functions and six engineering problems to show its performance. Additionally, several evaluation metrics were employed to ensure the efficiency and robustness of the proposed MRTMO. Furthermore, extensive comparisons with existing optimization algorithms were carried out to ensure the superiority of MRTMO. The numerical experiments proved the competitive performance of the proposed MRTMO in solving all tested CEC optimization and engineering problems.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 62150410434).

Author information

Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt

    Mohamed Abd Elaziz & Rehab Ali Ibrahim

  2. Faculty of Computer Science and Engineering, Galala University, Suez, 435611, Egypt

    Mohamed Abd Elaziz

  3. Faculty of Computer Sciences and Informatics, Amman Arab University, 11953, Amman, Jordan

    Laith Abualigah

  4. School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia

    Laith Abualigah

  5. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  6. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan

    Laith Abualigah

  7. Department of Computer, Damietta University, Damietta, 34517, Egypt

    Ahmed A Ewees

  8. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Mohammed AA Al-qaness

  9. Faculty of Engineering, Sana’a University, Sana’a, 12544, Yemen

    Mohammed AA Al-qaness

  10. Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt

    Reham R Mostafa

  11. Department of Electrical Engineering, Faculty of Engineering, Fayoum University, Fayoum, Egypt

    Dalia Yousri

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  1. Mohamed Abd Elaziz
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Correspondence to Mohammed AA Al-qaness.

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Appendix

Appendix

Table 16 Description of CEC2005 problems
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Table 17 Description of CEC2017 functions
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Elaziz, M.A., Abualigah, L., Ewees, A.A. et al. Triangular mutation-based manta-ray foraging optimization and orthogonal learning for global optimization and engineering problems. Appl Intell 53, 7788–7817 (2023). https://doi.org/10.1007/s10489-022-03899-1

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