Boosted Barnacles Algorithm Optimizer: Comprehensive Analysis for Social IoT Applications

The Social Internet of Things (SIoT) has revolutionized user experience through various applications and networking services like Health Monitoring, Assistance, Emergency Alert Systems, Collaborative Learning Platforms. However, transferring different types data between the interconnected objects in SIoT environment, including sensor data, user-generated social interaction poses challenges due to their high dimensionality. This paper presents an alternative method that improves resource efficiency, system performance, decision-making using Barnacles Mating Optimizer (BMO). BMO incorporates Triangular mutation dynamic Opposition-based learning enhance search space exploration prevent getting stuck local optima. Two experiments were conducted UCI datasets from SIoT-related datasets. results demonstrate developed method, DBMT, outperforms other algorithms predicting social-related IoT environment.