Three Dimensional Adaptive Path Planning Simulation Based on Ant Colony Optimization Algorithm

Oscar Guarnizo, Israel Pineda

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Ant Colony Optimization (ACO) is a metaheuristic widely used to solve different problems. This work proposes a three-dimensional simulation of adaptive path planning. New features were added to the basic ACO algorithm. First, the Random Walk based on visibility for initializing the pheromone matrix. The visibility of a node is the distance from the current node to the target node (dit) over the distance from the possible node to the target node (djt). The second feature is the inclusion of Killer Nodes for adaptive behavior. These nodes remove an ant and execute a decay function that removes the contributions over a wrong path. Finally, several experiments were performed to evaluate the solution accuracy, convergence time, and computational complexity. These results showed that the feasible ACO solution is near to the optimal solution with accuracy over 95% for most cases. It demonstrates that the algorithm provides promising results and finds a route after the addition of dynamic obstacles.

Idioma originalInglés
Título de la publicación alojada2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728156668
DOI
EstadoPublicada - nov. 2019
Publicado de forma externa
Evento6th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019 - Guayaquil, Ecuador
Duración: 11 nov. 201915 nov. 2019

Serie de la publicación

Nombre2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019

Conferencia

Conferencia6th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019
País/TerritorioEcuador
CiudadGuayaquil
Período11/11/1915/11/19

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