Local Search Effects in Bi-Objective Orienteering

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


Abstract
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.



Publication type
Research article in proceedings (conference)

Peer reviewed
Yes

Publication status
Published

Year
2018

Conference
Genetic and Evolutionary Computation Conference (GECCO '18)

Venue
Kyoto, Japan

Book title
Proceedings of the Genetic and Evolutionary Computation Conference

Start page
585

End page
592

Title of series
GECCO '18

Publisher
ACM

Place
New York, NY, USA

Language
English

ISBN
978-1-4503-5618-3

DOI