Comparative Analysis of BAS and PSO in Image Transformation Optimization

This paper presents a comparative study between the Particle Swarm Optimization (PSO) algorithm and the Beetle Antennae Search (BAS) algorithm for optimizing image transformations, with a focus on their performance in handling noisy and non-noisy images. Our experiments reveal that BAS consistently achieves better results in terms of pixel change when compared to PSO. The algorithms were evaluated based on their ability to minimize the objective function, which measures the error between the transformed reference image and the target image. Our results demonstrate that both BAS and PSO can effectively optimize image transformations, but BAS consistently outperformed PSO in terms of convergence speed and final objective value. Additional experiments with varying objective functions further validated the robustness and efficiency of BAS in achieving accurate image alignment.