2024
Kotze, André; Hildemann, Moritz Jan; Santos, Vitor; Granell-Canut, Carlos
Genetic Programming to Optimize 3D Trajectories Journal Article
In: ISPRS International Journal of Geo-Information, vol. 13, no. 8, pp. 295, 2024, ISSN: 2220-9964.
Abstract | Links | BibTeX | Tags: 3D, genetic algorithms, trajectory optimisation
@article{Kotze2024a,
title = {Genetic Programming to Optimize 3D Trajectories},
author = {André Kotze and Moritz Jan Hildemann and Vitor Santos and Carlos Granell-Canut},
doi = {https://doi.org/10.3390/ijgi13080295},
issn = {2220-9964},
year = {2024},
date = {2024-08-20},
journal = {ISPRS International Journal of Geo-Information},
volume = {13},
number = {8},
pages = {295},
abstract = {Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering.},
keywords = {3D, genetic algorithms, trajectory optimisation},
pubstate = {published},
tppubtype = {article}
}
Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering.
2017
Galvao, Marcelo; Ramos-Romero, Francisco; Lamar, Marcus; Taco, Pastor
Dynamic Visualization of Transit Information Using Genetic Algorithms for Path Schematization Proceedings Article
In: J. Horák I. Ivan, T. Inspektor (Ed.): Dynamics in GIscience. GIS OSTRAVA 2017, pp. 99-113, Springer, Cham, 2017, ISBN: 978-3-319-61296-6.
Abstract | Links | BibTeX | Tags: data visualization, genetic algorithms, Mastergeotech
@inproceedings{Galvao2017,
title = {Dynamic Visualization of Transit Information Using Genetic Algorithms for Path Schematization},
author = {Marcelo Galvao and Francisco Ramos-Romero and Marcus Lamar and Pastor Taco},
editor = {I. Ivan, J. Horák, T. Inspektor},
doi = {10.1007/978-3-319-61297-3_8},
isbn = {978-3-319-61296-6},
year = {2017},
date = {2017-08-24},
booktitle = {Dynamics in GIscience. GIS OSTRAVA 2017},
pages = {99-113},
publisher = {Springer},
address = {Cham},
abstract = {In this paper, we present a genetic algorithm for path octilinear simplification. The octilinear layout, recognized worldwide in metro maps, has the special property that edge orientations are restricted to eight angles. The proposed search technique combines possible solutions to find a solution with a desired balance between faithfulness to the original shape and reduction of bends along the path. We also aim the genetic algorithm to real-time response for dynamic web visualizations so we can experiment on how algorithms for the visualization of schematic maps can be availed in a context of mobile web devices in order to empower efficiency in transmitting transit and navigation information. A prototype of a web application and real transit data of the city of Castellón in Spain were used to test the methodology. The results have shown that real-time schematizations open possibilities concerning usability that add extra value to schematic transit maps. Additionally, performance tests show that the proposed genetic algorithms, if combined with topological data and scale variation transformation, are adequate to sketch bus transit maps automatically in terms of efficiency.},
keywords = {data visualization, genetic algorithms, Mastergeotech},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present a genetic algorithm for path octilinear simplification. The octilinear layout, recognized worldwide in metro maps, has the special property that edge orientations are restricted to eight angles. The proposed search technique combines possible solutions to find a solution with a desired balance between faithfulness to the original shape and reduction of bends along the path. We also aim the genetic algorithm to real-time response for dynamic web visualizations so we can experiment on how algorithms for the visualization of schematic maps can be availed in a context of mobile web devices in order to empower efficiency in transmitting transit and navigation information. A prototype of a web application and real transit data of the city of Castellón in Spain were used to test the methodology. The results have shown that real-time schematizations open possibilities concerning usability that add extra value to schematic transit maps. Additionally, performance tests show that the proposed genetic algorithms, if combined with topological data and scale variation transformation, are adequate to sketch bus transit maps automatically in terms of efficiency.