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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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11044352/ |
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| author | Desi W. Sari Suci Dwijayanti Bhakti Y. Suprapto |
| author_facet | Desi W. Sari Suci Dwijayanti Bhakti Y. Suprapto |
| author_sort | Desi W. Sari |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a69cad936bcc42b6b6e8e89359687c22 |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a69cad936bcc42b6b6e8e89359687c222025-07-17T23:02:07ZengIEEEIEEE Access2169-35362025-01-011310762110763010.1109/ACCESS.2025.358142511044352Integration of Regression-Based Guidance Ant for Enhanced Exploration and Convergence in Ant Colony Optimization (ACO)Desi W. Sari0https://orcid.org/0009-0000-9680-7068Suci Dwijayanti1https://orcid.org/0000-0003-2060-6408Bhakti Y. Suprapto2Department of Electrical Engineering, Universitas Sriwijaya, Palembang, South Sumatra, IndonesiaDepartment of Electrical Engineering, Universitas Sriwijaya, Palembang, South Sumatra, IndonesiaDepartment of Electrical Engineering, Universitas Sriwijaya, Palembang, South Sumatra, IndonesiaTraditional 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.https://ieeexplore.ieee.org/document/11044352/Autonomous vehiclespath planningregression-based guidancetraditional ACO |
| spellingShingle | Desi W. Sari Suci Dwijayanti Bhakti Y. Suprapto Integration of Regression-Based Guidance Ant for Enhanced Exploration and Convergence in Ant Colony Optimization (ACO) IEEE Access Autonomous vehicles path planning regression-based guidance traditional ACO |
| title | Integration of Regression-Based Guidance Ant for Enhanced Exploration and Convergence in Ant Colony Optimization (ACO) |
| title_full | Integration of Regression-Based Guidance Ant for Enhanced Exploration and Convergence in Ant Colony Optimization (ACO) |
| title_fullStr | Integration of Regression-Based Guidance Ant for Enhanced Exploration and Convergence in Ant Colony Optimization (ACO) |
| title_full_unstemmed | Integration of Regression-Based Guidance Ant for Enhanced Exploration and Convergence in Ant Colony Optimization (ACO) |
| title_short | Integration of Regression-Based Guidance Ant for Enhanced Exploration and Convergence in Ant Colony Optimization (ACO) |
| title_sort | integration of regression based guidance ant for enhanced exploration and convergence in ant colony optimization aco |
| topic | Autonomous vehicles path planning regression-based guidance traditional ACO |
| url | https://ieeexplore.ieee.org/document/11044352/ |
| work_keys_str_mv | AT desiwsari integrationofregressionbasedguidanceantforenhancedexplorationandconvergenceinantcolonyoptimizationaco AT sucidwijayanti integrationofregressionbasedguidanceantforenhancedexplorationandconvergenceinantcolonyoptimizationaco AT bhaktiysuprapto integrationofregressionbasedguidanceantforenhancedexplorationandconvergenceinantcolonyoptimizationaco |