The Archimedes Optimization Algorithm (AOA) is a recent physics-based metaheuristic inspired by Archimedes’ principle. Since its introduction by Hashim et al. in 2021, it has gained significant attention and has been applied to various real-world optimization problems. Its popularity stems from its simple structure, adaptability, ease of implementation, and satisfactory convergence. This paper presents a comprehensive review of the AOA algorithm, including its modified, multi-objective, and hybrid variants, and examines its applications in several domains such as classification, feature selection, parameter tuning, scheduling, photovoltaic systems, wireless networks, and clustering. The performance of the AOA algorithm is evaluated against some well-known metaheuristic algorithms, including Genetic Algorithm (GA), Differential Evolution (DE), Tabu Search (TS), Firefly Algorithm (FA), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Marine Predators Algorithm (MPA), and results demonstrates its superiority in most cases (72,22%) with stable dispersion in box-plot analyses. Finally, future research directions for improving the effectiveness and applicability of the AOA algorithm are outlined.

Skin cancer accounts for approximately half of all cancer cases worldwide, making it one of the most prevalent types of cancer. Melanoma, which develops from melanocytes that give skin its color, is the most lethal among skin cancers. Early diagnosis of melanoma, particularly through dermoscopy images, is of vital importance. To this end, automated diagnostic systems significantly aid dermatologists in their decision-making processes. In recent years, advancements in deep learning and machine learning have improved diagnostic accuracy. Specifically, CNN-based deep learning algorithms are utilized for medical image analysis and skin lesion segmentation. While traditional methods struggle to capture fine details and broader context, the U-Net architecture overcomes these challenges, providing more accurate segmentation. This study evaluates U-Net, Residual U-Net, and Attention U-Net models for skin lesion segmentation. The performance of the models is measured using Dice Score, Jaccard Index, and train loss metrics. The results reveal that Attention U-Net demonstrates the highest performance, with a Dice Score of 0.8063 and a Jaccard Index of 0.7203.