Hybrid Aquila optimizer with arithmetic optimization algorithm for global optimization tasks

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.

Automated summary

This paper proposes a new hybrid method using Aquila optimizer (AO) and arithmetic optimization algorithm (AOA), which can be applied to various problem domains. The research findings indicate that the proposed approach is effective for both high- and low-dimensional problems, achieving efficient search results.