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1- Teacher and researcher at the Imam Ja'far Sadiq Naval Special Training Center, Bushehr
2- Imam Khomeini University of Marine Science in Nowshahr & Naval Research Organization
3- Assistant Professor of the Faculty of Electrical Engineering at Zabol University
2- Imam Khomeini University of Marine Science in Nowshahr & Naval Research Organization
3- Assistant Professor of the Faculty of Electrical Engineering at Zabol University
Abstract: (57 Views)
| This paper investigates the automatic recognition of sonar targets using group classification that uses majority votes weighted and optimized by the Hiking Optimization Algorithm (HOA). Complex sonar targets and environmental challenges in the seas have increased the need for advanced AI techniques that are highly accurate and flexible. The HOA, which is based on the climbing model, strives to provide a more accurate optimization for the weighting of base classifications. This paper uses five basic classifiers, including XGBoost, LightGBM, CatBoost, Logistic Regression (LR), and Gradient Boosting Machine (GBM), which improve the accuracy of recognition and error reduction by combining them into an MV-HOA-based group classifier. The results show that the use of group classification method with HOA algorithm has significant capabilities in identifying sonar targets and can be useful for industrial and military applications. This innovative method not only optimizes classifier votes, but also reduces computational complexity and allows it to be applied in complex marine environments. |
Type of Study: Research Paper |
Subject:
Main Engine & Electrical Equipments
Received: 2025/05/12 | Accepted: 2025/10/18
Received: 2025/05/12 | Accepted: 2025/10/18
فایل صوتی چکیده مقاله [MP3 2138 KB] (5 Download)
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