@inproceedings{Ramirez-Atencia2017b,
title = {A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems},
author = {Cristian Ramirez-Atencia and Sanaz Mostaghim and David Camacho},
url = {http://doi.acm.org/10.1145/3071178.3071319},
doi = {10.1145/3071178.3071319},
isbn = {978-1-4503-4920-8},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {1216--1223},
publisher = {ACM},
address = {Berlin, Germany},
series = {GECCO '17},
abstract = {The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.},
keywords = {Constraint Satisfaction Problems, evolutionary multi-objective optimization, knee point, Mission Planning, Multi-objective Optimization, UAVs},
pubstate = {published},
tppubtype = {inproceedings}
}
The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.
