Evolution-inspired local improvement algorithm solving orienteering problem

Krzysztof Ostrowski (), Joanna Karbowska-Chilinska, Jolanta Koszelew and Pawel Zabielski
Additional contact information
Krzysztof Ostrowski: Bialystok University of Technology
Joanna Karbowska-Chilinska: Bialystok University of Technology
Jolanta Koszelew: Bialystok University of Technology
Pawel Zabielski: Bialystok University of Technology

Annals of Operations Research, 2017, vol. 253, issue 1, No 23, 519-543

Abstract: Abstract The orienteering problem (OP) is defined on a graph with scores assigned to the vertices and weights attached to the links. The objective of solutions to the OP is to find a route over a subset of vertices, limited in length, that maximizes the collective score of the vertices visited. In this paper we present a new, efficient method for solving the OP, called the evolution-inspired local improvement algorithm (EILIA). First, a multi-stage, hill climbing-based method is used to improve an initial random population of routes. During the evolutionary phase, both feasible and infeasible (routes that are too long) parts of the solution space are explored and exploited by the algorithm operators. Finally, infeasible routes are repaired by a repairing method. Computer testing of EILIA is conducted on popular data sets, as well as on a real transport network with 908 nodes proposed by the authors. The results are compared to an exact method (branch and cut) and to the best existing algorithms for OP. The results clearly show that EILIA outperforms existing heuristic methods in terms of the quality of its solutions. In many cases, EILIA produces the same results as the exact method.

Keywords: Travelling salesman problem; Orienteering problem; Optimization problem; Evolution-inspired local improvement algorithm; Trip planners (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-016-2278-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:253:y:2017:i:1:d:10.1007_s10479-016-2278-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-016-2278-1

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().