Local Search Effects in Bi-Objective Orienteering

Bossek Jakob, Grimme Christian, Meisel Stephan, Rudolph Guenter, Trautmann Heike

Forschungsartikel in Sammelband (Konferenz) | Peer reviewed

Zusammenfassung

We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process.

Details zur Publikation

BuchtitelProceedings of the Genetic and Evolutionary Computation Conference
Seitenbereich585-592
VerlagACM Press
ErscheinungsortNew York, NY, USA
Titel der ReiheGECCO '18
StatusVeröffentlicht
Veröffentlichungsjahr2018
Sprache, in der die Publikation verfasst istEnglisch
KonferenzGenetic and Evolutionary Computation Conference (GECCO '18), Kyoto, Japan
ISBN978-1-4503-5618-3
DOI10.1145/3205455.3205548

Autor*innen der Universität Münster

Bossek, Jakob
Professur für Statistik und Optimierung (Prof. Trautmann) (Statistik)
Grimme, Christian
Professur für Statistik und Optimierung (Prof. Trautmann) (Statistik)
Meisel, Stephan
Forschungsgruppe Quantitative Methoden in der Logistik (QML)
Trautmann, Heike
Professur für Statistik und Optimierung (Prof. Trautmann) (Statistik)