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Modified Flower Pollination Algorithm for Global Optimization

Author

Listed:
  • Mohamed Abdel-Basset

    (Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt)

  • Reda Mohamed

    (Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt)

  • Safaa Saber

    (Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt)

  • S. S. Askar

    (Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

  • Mohamed Abouhawwash

    (Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
    Department of Computational Mathematics, Science, and Engineering (CMSE), College of Engineering, Michigan State University, East Lansing, MI 48824, USA)

Abstract

In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases.

Suggested Citation

  • Mohamed Abdel-Basset & Reda Mohamed & Safaa Saber & S. S. Askar & Mohamed Abouhawwash, 2021. "Modified Flower Pollination Algorithm for Global Optimization," Mathematics, MDPI, vol. 9(14), pages 1-37, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:14:p:1661-:d:594520
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    References listed on IDEAS

    as
    1. L. Ingber, 2012. "Adaptive simulated annealing," Lester Ingber Papers 12as, Lester Ingber.
    2. Hu-Sheng Wu & Feng-Ming Zhang, 2014. "Wolf Pack Algorithm for Unconstrained Global Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-17, March.
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    Cited by:

    1. Mateusz Malarczyk & Grzegorz Kaczmarczyk & Jaroslaw Szrek & Marcin Kaminski, 2023. "Internet of Robotic Things (IoRT) and Metaheuristic Optimization Techniques Applied for Wheel-Legged Robot," Future Internet, MDPI, vol. 15(9), pages 1-19, September.

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