A flexible reference point-based multi-objective evolutionary algorithm: An application to the UAV route planning problem

•We develop a preference-based MOEA to converge to reference points.•The decision maker may change reference points throughout the algorithm.•The algorithm quickly adapts to changes in the reference points.•We develop specific mechanisms for route planning of unmanned air vehicles (UAVs).•This is th...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computers & operations research Jg. 114; S. 104811
Hauptverfasser: Dasdemir, Erdi, Köksalan, Murat, Tezcaner Öztürk, Diclehan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.02.2020
Pergamon Press Inc
Schlagworte:
Continuous terrain
Convergence
Decision making
Evolutionary algorithms
Genetic algorithms
Multi-objective evolutionary optimization
Multiple objective analysis
Operations research
Pareto optimum
Preference-based evolutionary algorithms
Radar detection
Reference point
Route planning
Terrain
UAV route planning
Unmanned aerial vehicles
UAV route planning
Multi-objective evolutionary optimization
Reference point
Preference-based evolutionary algorithms
Continuous terrain
ISSN:0305-0548, 1873-765X, 0305-0548
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•We develop a preference-based MOEA to converge to reference points.•The decision maker may change reference points throughout the algorithm.•The algorithm quickly adapts to changes in the reference points.•We develop specific mechanisms for route planning of unmanned air vehicles (UAVs).•This is the first preference-based MOEA developed for UAV route planning. We study the multi-objective route planning problem of an unmanned air vehicle (UAV) moving in a continuous terrain. In this problem, the UAV starts from a base, visits all targets and returns to the base in a continuous terrain that is monitored by radars. We consider two objectives: minimizing total distance and minimizing radar detection threat. This problem has infinitely many Pareto-optimal points and generating all those points is not possible. We develop a general preference-based multi-objective evolutionary algorithm to converge to preferred solutions. Preferences of a decision maker (DM) are elicited through reference point(s) and the algorithm converges to regions of the Pareto-optimal frontier close to the reference points. The algorithm allows the DM to change his/her reference point(s) whenever he/she so wishes. We devise mechanisms to prevent the algorithm from producing dominated points at the final population. We also develop mechanisms specific to the UAV route planning problem and test the algorithm on several UAV routing problems as well as other well-known problem instances. We demonstrate that our algorithm converges to preferred regions on the Pareto-optimal frontier and adapts to changes in the reference points quickly.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2019.104811