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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 3
  3. Swarm Intelligence : Volume 3, Issue 3, September 2009
  4. Metaheuristics for the bi-objective orienteering problem
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Swarm Intelligence : Volume 11
Swarm Intelligence : Volume 10
Swarm Intelligence : Volume 9
Swarm Intelligence : Volume 8
Swarm Intelligence : Volume 7
Swarm Intelligence : Volume 6
Swarm Intelligence : Volume 5
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 3, Issue 4, December 2009
Swarm Intelligence : Volume 3, Issue 3, September 2009
Metaheuristics for the bi-objective orienteering problem
Distributed scalable multi-robot learning using particle swarm optimization
Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem
Swarm Intelligence : Volume 3, Issue 2, June 2009
Swarm Intelligence : Volume 3, Issue 1, March 2009
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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Metaheuristics for the bi-objective orienteering problem

Content Provider SpringerLink
Author Schilde, Michael Doerner, Karl F. Hartl, Richard F. Kiechle, Guenter
Copyright Year 2009
Abstract In this paper, heuristic solution techniques for the multi-objective orienteering problem are developed. The motivation stems from the problem of planning individual tourist routes in a city. Each point of interest in a city provides different benefits for different categories (e.g., culture, shopping). Each tourist has different preferences for the different categories when selecting and visiting the points of interests (e.g., museums, churches). Hence, a multi-objective decision situation arises. To determine all the Pareto optimal solutions, two metaheuristic search techniques are developed and applied. We use the Pareto ant colony optimization algorithm and extend the design of the variable neighborhood search method to the multi-objective case. Both methods are hybridized with path relinking procedures. The performances of the two algorithms are tested on several benchmark instances as well as on real world instances from different Austrian regions and the cities of Vienna and Padua. The computational results show that both implemented methods are well performing algorithms to solve the multi-objective orienteering problem.
Starting Page 179
Ending Page 201
Page Count 23
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 3
Issue Number 3
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2009-05-15
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Ant colony optimization Variable neighborhood search Multiobjective optimization Orienteering problem Computer Communication Networks Communications Engineering, Networks ApplicationMathematics/Computational Methods of Engineering Computer Systems Organization and Communication Networks Artificial Intelligence (incl. Robotics)
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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