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Hybrid Aquila optimizer with arithmetic optimization algorithm for global optimization tasks

  • Optimization
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Abstract

Many population-dependent solutions have recently been suggested. Despite their widespread adoption in many applications, we are still researching using suggested methods to solve real-world problems. As a result, researchers must significantly adjust and refine their procedures based on the main evolutionary processes to ensure faster convergence, consistent equilibrium with high-quality results, and optimization. Thus, a new hybrid method using Aquila optimizer (AO) and arithmetic optimization algorithm (AOA) is proposed in this paper. AO and AOA are both modern meta-heuristic optimization methods. They can be applied to different problems, including image processing, machine learning, wireless networks, power systems, engineering design etc. The proposed approach is examined concerning AO and AOA. To determine results, each procedure is evaluated using the same parameters, such as population size and several iterations. By changing the dimensions, the proposed approach (AO–AOA) is evaluated. The impact of varying dimensions is a standard test that has been used in previous studies to optimize test functions that demonstrate the influence of varying dimensions on the efficiency of AO–AOA. It is clear from this that it fits well with both high- and low-dimensional problems. Population-based methods achieve efficient search results in high-dimensional problems.

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Acknowledgements

This study was financially supported via a funding grant by Deanship of Scientific Research, Taif University Researchers Supporting Project number (TURSP-2020/300), Taif University, Taif, Saudi Arabia.

Funding

The authors received no specific funding for this study. Taif University, TURSP-2020/300, Maryam Altalhi.

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Authors and Affiliations

  1. School of Electronics and Communication, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, 182320, India

    Shubham Mahajan & Amit Kant Pandit

  2. Faculty of Computer Science and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

  3. School of Computer Science, Universiti Sains Malaysia, Penang, Malaysia

    Laith Abualigah

  4. Department of Management Information System, College of Business Administration, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Maryam Altalhi

Authors
  1. Shubham Mahajan
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  2. Laith Abualigah
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  3. Amit Kant Pandit
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  4. Maryam Altalhi
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Correspondence to Shubham Mahajan.

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Mahajan, S., Abualigah, L., Pandit, A.K. et al. Hybrid Aquila optimizer with arithmetic optimization algorithm for global optimization tasks. Soft Comput 26, 4863–4881 (2022). https://doi.org/10.1007/s00500-022-06873-8

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