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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]