Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris’ Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon’s statistical test (P-value), and convergence curve. Based on the achieved results, CHHO confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets.

The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search Algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles’ encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic optimizer, namely the Improved Reptile Search Algorithm (IRSA), is proposed to overcome these limitations and adapt the RSA to solve the feature selection problem. Two main improvements adding value to the standard RSA; the first improvement is to apply the chaos theory at the initialization phase of RSA to enhance its exploration capabilities in the search space. The second improvement is to combine the Simulated Annealing (SA) algorithm with the exploitation search to avoid the local optima problem. The IRSA performance was evaluated over 20 medical benchmark datasets from the UCI machine learning repository. Also, IRSA is compared with the standard RSA and state-of-the-art optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization algorithm (GOA) and Slime Mould Optimization (SMO). The evaluation metrics include the number of selected features, classification accuracy, fitness value, Wilcoxon statistical test (p-value), and convergence curve. Based on the results obtained, IRSA confirmed its superiority over the original RSA algorithm and other optimized algorithms on the majority of the medical datasets.

The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets.

Maintaining a high input resolution is crucial for more complex tasks like detection or segmentation to ensure that models can adequately identify and reflect fine details in the output. This study aims to reduce the computation costs associated with high-resolution input by using a variant of transformer, known as the Adaptive Clustering Transformer (ACT). The proposed model is named ACT-FRCNN. Which integrates ACT with a Faster Region-Based Convolution Neural Network (FRCNN) for a detection task head. In this paper, we proposed a method to improve the detection framework, resulting in better performance for out-of-domain images, improved object identification, and reduced dependence on non-maximum suppression. The ACT-FRCNN represents a significant step in the application of transformer models to challenging visual tasks like object detection, laying the foundation for future work using transformer models. The performance of ACT-FRCNN was evaluated on a variety of well-known datasets including BSDS500, NYUDv2, and COCO. The results indicate that ACT-FRCNN reduces over-detection errors and improves the detection of large objects. The findings from this research have practical implications for object detection and other computer vision tasks.