Integration of Regression-Based Guidance Ant for Enhanced Exploration and Convergence in Ant Colony Optimization (ACO)
Traditional Ant Colony Optimization (T-ACO) algorithms often face challenges in dynamic environments, particularly the tendency to become trapped in local minima, resulting in suboptimal path planning. To address these limitations, this research incorporates a linear regression line as a directional...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11044352/ |
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| Summary: | Traditional Ant Colony Optimization (T-ACO) algorithms often face challenges in dynamic environments, particularly the tendency to become trapped in local minima, resulting in suboptimal path planning. To address these limitations, this research incorporates a linear regression line as a directional guide for ants, helping them navigate toward the optimal path more efficiently. This paper presents an improved Ant Colony Optimization (I-ACO) algorithm by integrating regression-based guidance to enhance both exploration and convergence. The proposed I-ACO method was tested using real-world geographic data from the Universitas Sriwijaya campus, and its performance was compared with that of T-ACO in both obstacle-free and obstacle-present scenarios. The results indicate that I-ACO consistently produces more accurate and efficient paths, with lower distance errors and faster computation times. Specifically, I-ACO achieved an average computation time of 0.8 seconds, compared to T-ACO’s 1.2 seconds. Moreover, I-ACO demonstrated superior adaptability when encountering obstacles, providing shorter and more efficient routes. These findings highlight the effectiveness of regression-based guidance in improving the performance of the ACO algorithm, making it more suitable for real-time autonomous vehicle navigation in complex environments. This approach not only enhances path accuracy but also reduces computational costs and improves obstacle avoidance capabilities. |
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| ISSN: | 2169-3536 |