Boosted Barnacles Algorithm Optimizer: Comprehensive Analysis for Social IoT Applications

Mohammed A. A. Al‐qaness

doi:10.1109/ACCESS.2023.3296255

Boosted Barnacles Algorithm Optimizer: Comprehensive Analysis for Social IoT Applications

2023-01-01

2

OA

AI

M

Mohammed A. A. Al‐qaness

Mohammed Azmi Al‐Betar

DOI：10.1109/ACCESS.2023.3296255

Original

Original request for help

Save

Abstract

En 中文

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

Keywords:

Social IoT

Barnacles Mating Optimizer

triangular mutation

opposition-based learning

Boosted Barnacles Algorithm Optimizer: Comprehensive Analysis for Social IoT Applications

Abstract

Journal

Organization