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Analysing the Robustness of Multiobjectivisation Approaches Applied to Large Scale Optimisation Problems

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EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 447))

Abstract

Multiobjectivisation transforms a mono-objective problem into a multiobjective one. The main aim of multiobjectivisation is to avoid stagnation in local optima, by changing the landscape of the original fitness function. In this contribution, an analysis of different multiobjectivisation approaches has been performed. It has been carried out with a set of scalable mono-objective benchmark problems. The experimental evaluation has demonstrated the advantages of multiobjectivisation, both in terms of quality and saved resources. However, it has been revealed that it produces a negative effect in some cases. Some multiobjectivisation schemes require the specification of additional parameters which must be adapted for dealing with different problems. Multiobjectivisation with parameters has been proposed as a method to improve the performance of the whole optimisation scheme. Nevertheless, the parameter setting of an optimisation scheme which considers multiobjectivisation with parameters is usually more complex. In this work, a new model based on the usage of hyperheuristics to facilitate the application of multiobjectivisation with parameters has been proposed. Experimental evaluation has shown that this model has increased the robustness of the whole optimisation scheme.

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Authors and Affiliations

  1. Dpto. de Estadística, I.O. y Computación, Universidad de La Laguna, La Laguna, 38271, Santa Cruz de Tenerife, Spain

    Carlos Segura, Eduardo Segredo & Coromoto León

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  1. Carlos Segura
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  2. Eduardo Segredo
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  3. Coromoto León
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Correspondence to Carlos Segura .

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  1. Computer Science and Communications, Research Unit, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Emilia Tantar

  2. , Computer Science and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourg

    Alexandru-Adrian Tantar

  3. Computer Science, and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Pascal Bouvry

  4. Bordeaux Mathematical Institute, INRIA Bordeaux-Sud Ouest, Université Bordeaux I, cours de la Libération 351, Talence Cedex, 33405, France

    Pierre Del Moral

  5. Bâtiment Leyteire, UFR Sciences et Modélisation, Université Bordeaux II, 3ter place de la Victoire, Bordeaux, 33000, France

    Pierrick Legrand

  6. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Carlos A. Coello Coello

  7. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Oliver Schütze

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Segura, C., Segredo, E., León, C. (2013). Analysing the Robustness of Multiobjectivisation Approaches Applied to Large Scale Optimisation Problems. In: Tantar, E., et al. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. Studies in Computational Intelligence, vol 447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32726-1_11

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  • DOI: https://doi.org/10.1007/978-3-642-32726-1_11

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