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Fusion of modern meta-heuristic optimization methods using arithmetic optimization algorithm for global optimization tasks

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Abstract

Several population-based techniques have subsequently been proposed. Despite their broad use in a variety of applications, we are still investigating the use of proposed methods to tackle real-world challenges. As a result, researchers must considerably modify and enhance their approaches based on the primary evolutionary processes in order to achieve rapid convergence, consistent equilibrium with high-quality data, and optimization. The paper proposes a fusion method (AOA–GOA) meta-heuristic optimization methods for global optimization tasks. They can be applied to different problems, including image processing, machine learning, wireless networks, power systems, engineering design, etc. The method fusion proposed is analyzed in context with GOA and AOA. To evaluate the performance, each method is tested on the same parameters like population size and number of iteration. The proposed IAOA is evaluated by varying the dimensions. The impact of varying dimensions is a standard test used in previous studies for optimizing test functions that show the effect of varying dimensions on efficiency of IAOA. From this, it is noted that it works efficiently for both high and low dimensional problems. In high dimensional problem, the proposed method gives efficient search results.

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Funding

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.

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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 Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia

    Laith Abualigah

  4. Department of Information Technology, School of Engineering and Technology, ALDAR University College, Garhoud – Dubai, UAE

    Mohammad Rustom Al Nasar

  5. IT Department, College of Engineering and IT, University of Dubai, Academic City, UAE

    Hamzah Ali Alkhazaleh

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

    Maryam Altalhi

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  1. Shubham Mahajan
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  2. Laith Abualigah
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  3. Amit Kant Pandit
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  4. Mohammad Rustom Al Nasar
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  5. Hamzah Ali Alkhazaleh
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  6. Maryam Altalhi
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All the authors have equally contributed in the study.

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Correspondence to Shubham Mahajan.

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Mahajan, S., Abualigah, L., Pandit, A.K. et al. Fusion of modern meta-heuristic optimization methods using arithmetic optimization algorithm for global optimization tasks. Soft Comput 26, 6749–6763 (2022). https://doi.org/10.1007/s00500-022-07079-8

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