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

Year 2025, Volume: 4 Issue: 1, 100 - 114, 18.02.2025

Gökhan Altun , İlhan Aydın

https://doi.org/10.62520/fujece.1501508

Abstract

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.

Keywords

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

Project Number

123E669

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

Abstract

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.

Keywords

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

Ethical Statement

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.

Supporting Institution

TUBITAK

Project Number

123E669

Thanks

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

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