Hybrid arithmetic optimization algorithm with hunger games search for global optimization

• Published in: Multimedia tools and applications Vol. 81; no. 20; pp. 28755 - 28778
• Main Authors: Mahajan, Shubham, Abualigah, Laith, Pandit, Amit Kant
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2022; Springer Nature B.V
• Subjects: Algorithms; Arithmetic; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Design engineering; Game theory; Games; Global optimization; Heuristic methods; Image processing; Machine learning; Mathematical analysis; Methods; Multimedia Information Systems; Optimization algorithms; Performance evaluation; Population; Searching; Special Purpose and Application-Based Systems; Wireless networks; Metaheuristic; Arithmetic optimization algorithm; Swarm intelligence; Engineering design problems; Optimization methods; Hunger games search
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-12922-z

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.