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Evolving a Pareto Front for an Optimal Bi-objective Robust Interception Problem with Imperfect Information

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

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 175))

Abstract

In this paper, a multi-objective optimal interception problem with imperfect information is solved by using a Multi-Objective Evolutionary Algorithm (MOEA). The traditional setting of the interception problem is aimed either at minimizing a miss distance for a given interception duration or at minimizing an interception time for a given miss distance. In contrast with such a setting, here the problem is posed as a simultaneous search for both objectives. Moreover, it is assumed that the interceptor has imperfect information on the target. This problem can be considered as a game between the interceptor, who is aiming at a minimum final distance between himself and the target at a minimal final time, and an artificial opponent aiming at maximizing these values. The artificial opponent represents the effect of the interceptor’s imperfect information (measurement inaccuracies) on the success of the interception. Both players utilize neural net controllers that evolve during the evolutionary optimization. This study is the first attempt to utilize evolutionary multi-objective optimization for solving multi-objective differential games, and as far as our review went, the first attempt to solve multi-objective differential games in general.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Ort Braude College, Karmiel, Israel

    Gideon Avigad, Erella Eisenstadt & Valery Y. Glizer

Authors
  1. Gideon Avigad
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  2. Erella Eisenstadt
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  3. Valery Y. Glizer
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Corresponding author

Correspondence to Gideon Avigad .

Editor information

Editors and Affiliations

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

    Oliver Schütze

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

    Carlos A. Coello Coello

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

    Alexandru-Adrian Tantar

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

    Emilia Tantar

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

    Pascal Bouvry

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

    Pierre Del Moral

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

    Pierrick Legrand

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Avigad, G., Eisenstadt, E., Glizer, V.Y. (2013). Evolving a Pareto Front for an Optimal Bi-objective Robust Interception Problem with Imperfect Information. In: Schütze, O., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Advances in Intelligent Systems and Computing, vol 175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31519-0_8

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  • DOI: https://doi.org/10.1007/978-3-642-31519-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31518-3

  • Online ISBN: 978-3-642-31519-0

  • eBook Packages: EngineeringEngineering (R0)

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