Abstract—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 is demonstrated through simulations under
different scenarios. The results show that our optimized GPU
based parallel solution for route planning problem is up to 1.6
times faster than serial and parallel CPU solutions. Moreover,
our optimized GPU solution is better on cost than other
solutions. It is shown that our GPU based approach is the
fastest one and increases performance thanks to the massive
parallelization capabilities of GPUs.
Index Terms—GPU programming, parallel programming, route planning, simulated annealing.
Seval Capraz is with Ante Grup Bilisim Ticaret A.S., Ankara, Turkey. She is also with the Department of Computer Engineering, Hacettepe University, Ankara, Turkey (e-mail: firstname.lastname@example.org). Halil Azyikmis and Adnan Ozsoy are with the Department of Computer Engineering, Hacettepe University, Ankara, Turkey (e-mail: email@example.com, firstname.lastname@example.org).
Cite: Seval Capraz, Halil Azyikmis, and Adnan Ozsoy, "An Optimized GPU-Accelerated Route Planning of Multi-UAV Systems Using Simulated Annealing," International Journal of Machine Learning and Computing vol. 10, no. 3, pp. 471-476, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).