TY - GEN

T1 - Application of the Simulated Annealing Algorithm for Finding the Optimal Trajectory in the Sense of Construction Cost

AU - Аббасов, Меджид Эльхан оглы

AU - Рычков, Андрей Сергеевич

PY - 2025

Y1 - 2025

N2 - We consider variational problem of finding the cost-optimal path on the surface of the terrain. In our model the path is represented as a curve on a plain, while the actual terrain is taken into consideration by the cost function. Our main goal is to obtain an approximate solution as a piecewise linear function via simulated annealing algorithm. For this purpose, we introduce a uniform grid and solve a problem of finding least-cost path with transition prices obtained as the values of the integral cost functional. We adapt the simulated annealing algorithm for this problem and compare its performance with a set of other solutions, namely modified A* and ant colony optimization algorithm. To obtain a better solution we use modifications of the algorithm, such as quantum annealing and stochastic tunneling, which help us improve the performance and avoid getting stuck in the local optima. We also compare the used approaches and provide numerical examples of application of the introduced methods.

AB - We consider variational problem of finding the cost-optimal path on the surface of the terrain. In our model the path is represented as a curve on a plain, while the actual terrain is taken into consideration by the cost function. Our main goal is to obtain an approximate solution as a piecewise linear function via simulated annealing algorithm. For this purpose, we introduce a uniform grid and solve a problem of finding least-cost path with transition prices obtained as the values of the integral cost functional. We adapt the simulated annealing algorithm for this problem and compare its performance with a set of other solutions, namely modified A* and ant colony optimization algorithm. To obtain a better solution we use modifications of the algorithm, such as quantum annealing and stochastic tunneling, which help us improve the performance and avoid getting stuck in the local optima. We also compare the used approaches and provide numerical examples of application of the introduced methods.

KW - Calculus of Variations

KW - Mathematical Modelling

KW - Optimal Trajectory

KW - Quantum Annealing

KW - Simulated Annealing

UR - https://www.mendeley.com/catalogue/5f9dddb9-d7d0-35fe-a77c-65c3e098a9ce/

U2 - 10.1007/978-3-031-73417-5_24

DO - 10.1007/978-3-031-73417-5_24

M3 - Conference contribution

SN - 9783031734168

T3 - Communications in Computer and Information Science

SP - 313

EP - 323

BT - Information Technologies and Their Applications (ITTA 2024)

Y2 - 23 April 2024 through 25 April 2024

ER -