Feature selection is a significant issue in the machine learning process. Most datasets include features that are not needed for the problem being studied. These irrelevant features reduce both the efficiency and accuracy of the algorithm. It is possible to think about feature selection as an optimization problem. Swarm intelligence algorithms are promising techniques for solving this problem. This research paper presents a hybrid approach for tackling the problem of feature selection. A filter method (chi-square) and two wrapper swarm intelligence algorithms (grey wolf optimization (GWO) and particle swarm optimization (PSO)) are used in two different techniques to improve feature selection accuracy and system execution time. The performance of the two phases of the proposed approach is assessed using two distinct datasets. The results show that PSOGWO yields a maximum accuracy boost of 95.3%, while chi2-PSOGWO yields a maximum accuracy improvement of 95.961% for feature selection. The experimental results show that the proposed approach performs better than the compared approaches.

Healthcare services (HCS) based on cloud computing and the Internet of Things are a great opportunity for the development of medical information technology. Task scheduling in cloud computing is one of the most critical problems facing health care services, as it affects the time required to fulfill user requests and the cost and quality of service delivery. The proposed HCS model structure consists of major components such as user devices, user requests, cloud broker, IoT endpoints, and HCS cloud. This paper proposes a new method to improve task scheduling in healthcare services based on cloud computing in the IoT environment (cloud-IoT). Specifically, A hybrid optimization algorithm HPSOSSA is proposed that combines the best existing swarm intelligence algorithms and integrates the advantages of particle swarm optimization (PSO) and the Salp Swarm Algorithm (SSA). The proposed model was implemented using the Cloudsim simulation package run on Eclipse with specific parameters. The proposed hybrid algorithm was compared to the most popular optimization algorithms that were previously used, such as Ant Colony Optimization (ACO), PSO, SSA, and hybrid PSO-GA. The experimental results showed that HPSOSSA in all cases outperforms the other existing algorithms in terms of makespan, waiting time, and resource utilization.

Data clustering is an unsupervised learning task that has been extensively studied, given its wide applicability in various domains. Traditional algorithms often struggle to achieve a balance between exploration and exploitation, leading to sub-optimal solutions. This paper presents a novel hybrid algorithm named SA-PSO-GK++ that synergistically combines Particle Swarm Optimization (PSO), K-means++, Simulated Annealing (SA), and Gaussian Estimation of Distribution to tackle this issue effectively. The proposed SA-PSO-GK++ aims to overcome the drawbacks of existing methods by leveraging the strengths of each individual algorithm. The K-means++ initialization reduces the risk of poor initial centroids, while PSO aids in efficient search space exploration. GED provides a statistical model of the particle space, enabling the algorithm to generate new potential solutions that are statistically guided by the current best solutions. Additionally, the incorporation of Simulated Annealing allows the algorithm to escape local minima, thereby enhancing its global search capability. We evaluate the effectiveness of SA-PSO-GK++ using benchmark datasets from the UCI Machine Learning Repository, including the Iris, Breast cancer, Heart datasets and contraceptive method choice datasets. The proposed method outperforms conventional and some of the state-of-the-art hybrid clustering algorithms in terms of sum of euclidean distance, normalized index, and error rates. These advantages make SA-PSO-GK++ a compelling option for a wide range of clustering applications. The results offer promising avenues for future research in optimizing and applying this innovative clustering technique in diverse domains.