İnsansız Araç Navigasyonunun Optimize Edilmesi: Verimli Rota Planlaması için Hibrit PSO-GWO Algoritması

Öz

Bu çalışma, insansız araçların kullanımında önemli bir yere sahip olan otonom sistemler için rota planlama problemini ele almayı amaçlamaktadır. Belirtilen problemin çözümünde kullanılacak olan meta-sezgisel algoritma yaklaşımlarının performansını artırmak amacıyla hibrit bir algoritma önerilmiştir. Önerilen hibrit algoritmada, Parçacık Sürü Optimizasyonu (PSO) algoritmasının basit kullanımı ve güçlü küresel arama yetenekleri, Gri Kurt Optimizasyonu (GKO) algoritmasının güçlü keşif ve yerel minimumdan kaçınma özellikleriyle birleştirilmiştir. Önerilen hibrit yaklaşım, hem hesaplama doğruluğunu hem de işlem süresinde verimliliği sağlamayı hedeflemektedir. Hibrit yaklaşım kullanılarak, bilinmeyen bir ortamda sensörler yardımıyla rotalar hesaplanmıştır. Hibrit algoritmanın performansı, bireysel PSO ve GKO algoritmaları ile karşılaştırılmıştır. Karşılaştırma sırasında algoritmalar; optimum rotayı bulma süreleri, hesaplanan rota uzunluğu, gerekli iterasyon sayısı ve yerel minimumdan kaçınma yetenekleri açısından değerlendirilmiştir. Sonuçlar, özel olarak geliştirilmiş bir arayüz kullanılarak simüle edilmiş ve rota hesaplama süresi açısından önemli bir avantaj sağlandığı gözlemlenmiştir. Ayrıca, PSO yaklaşımında mevcut olan yerel minimum problemi başarılı bir şekilde ortadan kaldırılmış ve GKO yaklaşımına kıyasla iterasyon sayısı ile işlem süresi iyileştirilmiştir. Bu yaklaşımın, özellikle afet yönetimi senaryolarında fayda sağlaması beklenmektedir. Çünkü otonom insansız araçlar, arama, kurtarma ve kaynak dağıtımı için bilinmeyen veya engelli ortamlarda verimli rota planlaması yapılmasına yardımcı olabilir.

Anahtar Kelimeler

• Gri kurt optimizasyonu
• Rota planlama
• İnsansız hava aracı
• Hibrit algoritma.
• Parçacık sürü optimizasyonu

Proje Numarası

123E669

Optimizing Unmanned Vehicle Navigation: A Hybrid PSO-GWO Algorithm for Efficient Route Planning

Öz

This study aims to address the route-planning problem for autonomous systems, which plays a significant role in the operation of unmanned vehicles. A hybrid algorithm has been proposed to enhance the performance of metaheuristic algorithm approaches used to solve the specified problem. In the hybrid algorithm, the simplicity and powerful global search capabilities of the Particle Swarm Optimization (PSO) algorithm are combined with the strong exploration and local minimum avoidance features of the Grey Wolf Optimization (GWO) algorithm. The proposed hybrid approach seeks to achieve both computational accuracy and efficiency in processing time. Using the hybrid approach, routes were calculated in an unknown environment with the help of sensors. The performance of the hybrid algorithm was compared with that of the standalone PSO and GWO algorithms. The comparison evaluated the algorithms based on their execution time for finding the optimal route, the length of the calculated route, the required number of iterations, and their ability to escape local minima. The results were simulated using a custom-built interface, demonstrating a significant advantage in terms of route calculation time. Furthermore, the local minimum problem inherent in the PSO approach was successfully mitigated, while the iteration count and processing time were improved compared to the GWO approach. This approach can be particularly beneficial in disaster management scenarios, where autonomous unmanned vehicles can assist in efficiently planning routes for search, rescue, and resource delivery in unknown or obstructed environments.

Anahtar Kelimeler

• Particle swarm optimization
• Grey wolf optimization
• Route planning
• Unmanned aerial vehicle
• Hybrid algorithm

Destekleyen Kurum

TUBITAK

Proje Numarası

123E669

Etik Beyan

This study does not involve human or animal subjects, which requires ethics committee approval. All data used in the study were obtained by the authors in a two-dimensional and simulation environment and no personal data were used.

Teşekkür

This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project No. 123E669.

Kaynakça

1. N. L. Biggs, E. K. Lloyd, and R. J. Wilson, Graph Theory, 1736-1936, 1986.
2. F. A. Şenel, F. Gökçe, A. S. Yüksel, and T. Yiğit, "A Novel Hybrid PSO–GWO Algorithm for Optimization Problems," Engineering with Computers, vol. 35, no. 4, pp. 1359–1373, Dec. 2018.
3. V. K. Kamboj, "A Novel Hybrid PSO–GWO Approach for Unit Commitment Problem," Neural Computing and Applications, vol. 27, no. 6, pp. 1643–1655, Jun. 2015.
4. S. Mahapatra, M. Badi, and S. Raj, "Implementation of PSO, it’s variants and Hybrid GWOPSO for improving Reactive Power Planning," in 2019 Global Conference for Advancement in Technology (GCAT), pp. 1–6.
5. N. Singh and S. B. Singh, "Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance," Journal of Applied Mathematics, vol. 2017, p. e2030489, Nov. 2017.
6. D.-T. Nguyen, J.-R. Ho, P.-C. Tung, and C.-K. Lin, "A Hybrid PSO–GWO Fuzzy Logic Controller with a New Fuzzy Tuner," International Journal of Fuzzy Systems, vol. 24, no. 3, pp. 1586–1604, Nov. 2021.
7. G. Negi, A. Kumar, S. Pant, and M. Ram, "Optimization of Complex System Reliability Using Hybrid Grey Wolf Optimizer," Decision Making: Applications in Management and Engineering, vol. 4, no. 2, pp. 241–256, Oct. 2021.
8. A. Thobiani, S. Khatir, B. Benaissa, E. Ghandourah, S. Mirjalili, and A. Wahab, "A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification," Theoretical and Applied Fracture Mechanics, vol. 118, p. 103213, 2022.

9. F. Gul, W. Rahiman, S. S. N. Alhady, A. Ali, I. Mir, and A. Jalil, "Meta-heuristic Approach for Solving multi-objective Path Planning for Autonomous Guided Robot Using PSO–GWO Optimization Algorithm with Evolutionary Programming," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 7, pp. 7873–7890, Sep. 2020.
10. B. Liu and X. Wang, "UAV Route Planning Based on QPSO Algorithm under Rolling Time Domain Control," in 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM), pp. 612–617.
11. S. Chen, Z. Yang, Z. Liu, and H. Jin, "An improved artificial potential field based path planning algorithm for unmanned aerial vehicle in dynamic environments," in 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), pp. 591–596.
12. L. Zhang, Y. Zhang, and Y. Li, "Mobile Robot Path Planning Based on Improved Localized Particle Swarm Optimization," IEEE Sensors Journal, vol. 21, no. 5, pp. 6962–6972, 2021.
13. H. Xu, S. Jiang, and A. Zhang, "Path Planning for Unmanned Aerial Vehicle Using a MixStrategyBased Gravitational Search Algorithm," IEEE Access, vol. 9, pp. 57033–57045, 2021.
14. H. Tang, W. Sun, A. Lin, M. Xue, and X. Zhang, "A GWO based multirobot cooperation method for target searching in unknown environments," Expert Systems with Applications, vol. 186, p. 115795, 2021.
15. L. He, N. Aouf, and B. Song, "Explainable Deep Reinforcement Learning for UAV autonomous path planning," Aerospace Science and Technology, vol. 118, p. 107052, 2021.
16. Z. Garip, D. Karayel, and M. Erhan Çimen, "A Study on Path Planning Optimization of Mobile Robots Based on Hybrid Algorithm," Concurrency and Computation: Practice and Experience, vol. 34, no. 5, Nov. 2021.
17. T. Yılmaz and Ö. Aydoğmus, "Deep Deterministic Policy Gradient Reinforcement Learning for collision-free Navigation of Mobile Robots in Unknown Environments," Firat University Journal of Experimental and Computational Engineering, vol. 2, no. 2, pp. 87–96, Jan. 2023.
18. X. Sun, S. Pan, N. Bao, and N. Liu, "Hybrid Ant Colony and Intelligent Water Drop Algorithm for Route Planning of Unmanned Aerial Vehicles," Computers & Electrical Engineering, vol. 111, pp. 108957–108957, Nov. 2023.
19. R. Wan, X. Wang, and Z. Ma, "UAV route planning based on improved whale optimization algorithm and dynamic artificial potential field method," in 2023 6th International Symposium on Autonomous Systems (ISAS), pp. 1–8.
20. R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43.
21. S. Mirjalili, Seyed Mohammad Mirjalili, and A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, vol. 69, pp. 46–61, 2014.
22. B. N. Gohil and D. R. Patel, "A hybrid GWOPSO Algorithm for Load Balancing in Cloud Computing Environment," in 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 185–191.