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On the Performance of Local Search for the Biobjective Traveling Salesman Problem

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Advances in Multi-Objective Nature Inspired Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 272))

Summary

In this chapter we investigate experimentally the performance of multiobjective local search approaches that are based on the component-wise acceptance criterion search model. This model gives a framework for many well-known evolutionary and local search algorithms. Using the biobjective traveling salesman problem as an example application, we analyse the impact of three important algorithmic components on the performance of a simple local search algorithm that follows this search model: initialization strategy, neighborhood structure and archive bounding. By following principles of experimental design, we study the effects of each component, both in terms of solution quality and computation time. The experimental analysis indicates the existence of several complex trade-offs between solution quality and run-time for many of the choices available for each component.

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Authors and Affiliations

  1. CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Luís Paquete

  2. IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Thomas Stützle

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  1. Luís Paquete
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  2. Thomas Stützle
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Editors and Affiliations

  1. Depto. de Computación, CINVESTAV-IPN, Av. IPN No. 2508, Col. San Pedro Zacatenco, 07300, México, D.F., Mexico

    Carlos A. Coello Coello

  2. Parc scientifique de la Haute-Borne, LIFL/CNRS/Polytech’Lille/INRIA, Bâtiment A, Park Plaza, 40,Avenue Halley, 59650, Villeneuve d’Ascq, France

    Clarisse Dhaenens

  3. Parc scientifique de la Haute-Borne, INRIA/LIFL/CNRS, Bâtiment A, Park Plaza, 40, Avenue Halley, 59650, Villeneuve d’Ascq, France

    Laetitia Jourdan

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Paquete, L., Stützle, T. (2010). On the Performance of Local Search for the Biobjective Traveling Salesman Problem. In: Coello Coello, C.A., Dhaenens, C., Jourdan, L. (eds) Advances in Multi-Objective Nature Inspired Computing. Studies in Computational Intelligence, vol 272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11218-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-11218-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11217-1

  • Online ISBN: 978-3-642-11218-8

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