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Hybrid arithmetic optimization algorithm with hunger games search for global optimization

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Abstract

Recently, many population-dependent methods have been proposed. Despite their acceptance in many applications, we are still exploring suggested methods to solve actual problems. Consequently, researchers need to change and refine their procedures significantly based on the major evolutionary processes to achieve quicker convergence, more consistent equilibrium with high-quality performance and optimization. Therefore, a new hybrid method using Hunger Games Search (HGS) and Arithmetic Optimization Algorithm (AOA) is proposed in this paper. HGS is a recently proposed population-dependent optimization method that stabilizes the features and efficiently performs unconstrained and constrained problems. In contrast, AOA is a modern meta-heuristic optimization method. They can be applied to different problems, including image processing, machine learning, wireless networks, power systems, engineering design etc. The proposed method is analyzed in context with HGS and AOA. Each method is tested on the same parameters like population size and no. of iteration to evaluate the performance. The proposed method (AOA-HGS) is assessed by varying the dimensions on 23 functions (F1-F23). The impact of varying dimensions is a standard test utilized in previous studies for optimizing test functions that show the effect of varying dimensions on the efficiency of AOA-HGS. From this, it is noted that it works efficiently for both high and low dimensional problems. In high dimensional problem population, dependent methods give efficient search results. The AOA-HGS is very competitive and superior compared with others on test functions. No. of optimization methods obtained optimum results, but AOA-HGS has the best when compared with all. So, AOA-HGS is capable of getting optimum results.

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Authors and Affiliations

  1. School of Electronics & Communication, Shri Mata Vaishno Devi University, Katra, J&K, 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

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  1. Shubham Mahajan
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  2. Laith Abualigah
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Mahajan, S., Abualigah, L. & Pandit, A.K. Hybrid arithmetic optimization algorithm with hunger games search for global optimization. Multimed Tools Appl 81, 28755–28778 (2022). https://doi.org/10.1007/s11042-022-12922-z

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