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An Optimized GPU-Accelerated Route Planning of Multi-UAV Systems Using Simulated Annealing Article CUDA CODE

Usage of multiple unmanned aerial vehicles (UAV) in a certain mission makes flight route planning more complicated and slower. In order to obtain better performance, in the literature, most of the researchers propose using evolutionary algorithms and artificial intelligence approaches based on heuristics as optimization techniques. In addition to this, parallel programming approaches increase the computation performance. Therefore, this study focuses to discuss and solve the route planning problem for multi-UAV systems by using optimization techniques based on an evolutionary algorithm: simulated annealing. The travel cost and execution time are downsized in this work by optimization on algorithm and code. We implemented CPU based parallel solution to compare results with the GPU-accelerated one. The efficiency and the effectiveness of our parallelized and optimized solution.

Features:

• CUDA
• GPU
• Simulated Annealing
• Parallel Programming
• Route Planning
• Multi UAVs

User interface: Console/Terminal.
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