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Development and Applications of Augmented Whale Optimization Algorithm

Author

Listed:
  • Khalid Abdulaziz Alnowibet

    (Statistics and Operations Research Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Shalini Shekhawat

    (Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur 302017, Rajasthan, India)

  • Akash Saxena

    (Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur 302017, Rajasthan, India)

  • Karam M. Sallam

    (School of IT and Systems, University of Canberra, Canberra, ACT 2601, Australia)

  • Ali Wagdy Mohamed

    (Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt
    Department of Mathematics and Actuarial Science School of Sciences Engineering, The American University in Cairo, Cairo 11835, Egypt)

Abstract

Metaheuristics are proven solutions for complex optimization problems. Recently, bio-inspired metaheuristics have shown their capabilities for solving complex engineering problems. The Whale Optimization Algorithm is a popular metaheuristic, which is based on the hunting behavior of whale. For some problems, this algorithm suffers from local minima entrapment. To make WOA compatible with a number of challenging problems, two major modifications are proposed in this paper: the first one is opposition-based learning in the initialization phase, while the second is inculcation of Cauchy mutation operator in the position updating phase. The proposed variant is named the Augmented Whale Optimization Algorithm (AWOA) and tested over two benchmark suits, i.e., classical benchmark functions and the latest CEC-2017 benchmark functions for 10 dimension and 30 dimension problems. Various analyses, including convergence property analysis, boxplot analysis and Wilcoxon rank sum test analysis, show that the proposed variant possesses better exploration and exploitation capabilities. Along with this, the application of AWOA has been reported for three real-world problems of various disciplines. The results revealed that the proposed variant exhibits better optimization performance.

Suggested Citation

  • Khalid Abdulaziz Alnowibet & Shalini Shekhawat & Akash Saxena & Karam M. Sallam & Ali Wagdy Mohamed, 2022. "Development and Applications of Augmented Whale Optimization Algorithm," Mathematics, MDPI, vol. 10(12), pages 1-33, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2076-:d:839599
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    References listed on IDEAS

    as
    1. David H. Wolpert & William G. Macready, 1995. "No Free Lunch Theorems for Search," Working Papers 95-02-010, Santa Fe Institute.
    2. Oliva, Diego & Abd El Aziz, Mohamed & Ella Hassanien, Aboul, 2017. "Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm," Applied Energy, Elsevier, vol. 200(C), pages 141-154.
    3. Ali Azadeh & Seyed Mohammad Asadzadeh & Rana Jalali & Samira Hemmati, 2014. "A greedy randomised adaptive search procedure - genetic algorithm for electricity consumption estimation and optimisation in agriculture sector with random variation," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 17(3), pages 285-301.
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    Cited by:

    1. Shoyab Ali & Annapurna Bhargava & Akash Saxena & Pavan Kumar, 2023. "A Hybrid Marine Predator Sine Cosine Algorithm for Parameter Selection of Hybrid Active Power Filter," Mathematics, MDPI, vol. 11(3), pages 1-25, January.
    2. Jian Dong, 2023. "Preface to the Special Issue on “Recent Advances in Swarm Intelligence Algorithms and Their Applications”—Special Issue Book," Mathematics, MDPI, vol. 11(12), pages 1-4, June.
    3. Akash Saxena & Ahmad M. Alshamrani & Adel Fahad Alrasheedi & Khalid Abdulaziz Alnowibet & Ali Wagdy Mohamed, 2022. "A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines," Mathematics, MDPI, vol. 10(15), pages 1-16, August.

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