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Enhanced Marine Predators Algorithm for Solving Global Optimization and Feature Selection Problems

Author

Listed:
  • Ahmed A. Ewees

    (Department of Information Systems, College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia
    Department of Computer, Damietta University, Damietta 34517, Egypt)

  • Fatma H. Ismail

    (Faculty of Computer Science, Misr International University, Cairo 11341, Egypt)

  • Rania M. Ghoniem

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
    Department of Computer, Mansoura University, Mansoura 35516, Egypt)

  • Marwa A. Gaheen

    (Department of Computer, Damietta University, Damietta 34517, Egypt)

Abstract

Feature selection (FS) is applied to reduce data dimensions while retaining much information. Many optimization methods have been applied to enhance the efficiency of FS algorithms. These approaches reduce the processing time and improve the accuracy of the learning models. In this paper, a developed method called MPAO based on the marine predators algorithm (MPA) and the “narrowed exploration” strategy of the Aquila optimizer (AO) is proposed to handle FS, global optimization, and engineering problems. This modification enhances the exploration behavior of the MPA to update and explore the search space. Therefore, the narrowed exploration of the AO increases the searchability of the MPA, thereby improving its ability to obtain optimal or near-optimal results, which effectively helps the original MPA overcome the local optima issues in the problem domain. The performance of the proposed MPAO method is evaluated on solving FS and global optimization problems using some evaluation criteria, including the maximum value (Max), minimum value (Min), and standard deviation (Std) of the fitness function. Furthermore, the results are compared to some meta-heuristic methods over four engineering problems. Experimental results confirm the efficiency of the proposed MPAO method in solving FS, global optimization, and engineering problems.

Suggested Citation

  • Ahmed A. Ewees & Fatma H. Ismail & Rania M. Ghoniem & Marwa A. Gaheen, 2022. "Enhanced Marine Predators Algorithm for Solving Global Optimization and Feature Selection Problems," Mathematics, MDPI, vol. 10(21), pages 1-21, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:4154-:d:965301
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    References listed on IDEAS

    as
    1. Mohammad H. Nadimi-Shahraki & Shokooh Taghian & Seyedali Mirjalili & Laith Abualigah, 2022. "Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study," Mathematics, MDPI, vol. 10(11), pages 1-24, June.
    2. Adel Fahad Alrasheedi & Khalid Abdulaziz Alnowibet & Akash Saxena & Karam M. Sallam & Ali Wagdy Mohamed, 2022. "Chaos Embed Marine Predator (CMPA) Algorithm for Feature Selection," Mathematics, MDPI, vol. 10(9), pages 1-18, April.
    3. Al-qaness, Mohammed A.A. & Ewees, Ahmed A. & Fan, Hong & Abualigah, Laith & Elaziz, Mohamed Abd, 2022. "Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting," Applied Energy, Elsevier, vol. 314(C).
    4. Md Reyaz Hussan & Mohammad Irfan Sarwar & Adil Sarwar & Mohd Tariq & Shafiq Ahmad & Adamali Shah Noor Mohamed & Irfan A. Khan & Mohammad Muktafi Ali Khan, 2022. "Aquila Optimization Based Harmonic Elimination in a Modified H-Bridge Inverter," Sustainability, MDPI, vol. 14(2), pages 1-16, January.
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    Cited by:

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