Local Search Effects in Bi-Objective Orienteering

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


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.



Publikationstyp
Aufsatz (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2018

Konferenz
Genetic and Evolutionary Computation Conference (GECCO '18)

Konferenzort
Kyoto, Japan

Buchtitel
Proceedings of the Genetic and Evolutionary Computation Conference

Erste Seite
585

Letzte Seite
592

Reihe
GECCO '18

Verlag
ACM

Ort
New York, NY, USA

ISBN
978-1-4503-5618-3

DOI