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Title of Journal: Ann Oper Res

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Abbravation: Annals of Operations Research

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Springer US

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DOI

10.1002/jor.1100140106

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1572-9338

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Evolutioninspired local improvement algorithm sol

Authors: Krzysztof Ostrowski Joanna KarbowskaChilinska Jolanta Koszelew Pawel Zabielski
Publish Date: 2016/08/01
Volume: 253, Issue: 1, Pages: 519-543
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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 evolutioninspired local improvement algorithm EILIA First a multistage hill climbingbased 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 methodThe authors would like to thank Vicente Campos Rafael Marti Jess SnchezOro and Abraham Duarte for executing their algorithms Campos et al 2014 on our network 908 cities of Poland and sharing the results with us The authors gratefully acknowledge support from the Polish Ministry of Science and Higher Education at the Bialystok University of Technology Grant S/WI/1/2014 W/WI/2/2013 and W/WI/4/2014

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