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Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms

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Soft Computing in Engineering Design and Manufacturing

Abstract

This paper investigates the problem of using a genetic algorithm to converge on a small, user-defined subset of acceptable solutions to multiobjective problems, in the Pareto-optimal (P-O) range. The paper initially explores exactly why separate objectives can cause problems in a genetic algorithm (GA). A technique to guide the GA to converge on the subset of acceptable solutions is then introduced.

The paper then describes the application of six multiobjective techniques (three established methods and three new, or less commonly used methods) to four test functions. The previously unpublished distribution of solutions produced in the P-O range(s) by each method is described. The distribution of solutions and the ability of each method to guide the GA to converge on a small, user-defined subset of P-O solutions is then assessed, with the conclusion that two of the new multiobjective ranking methods are most useful.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    P. J. Bentley

  2. Division of Computing and Control Systems, School of Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK

    J. P. Wakefield

Authors
  1. P. J. Bentley
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  2. J. P. Wakefield
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Editor information

Editors and Affiliations

  1. School of Mechanical Engineering, University of Bath, Bath, BA2 7AY, UK

    P. K. Chawdhry Ph.D.

  2. The CIM Institute, Cranfield University, Cranfield, Bedford, MK43 0AL, UK

    R. Roy Ph.D.

  3. Department of Aerospace Engineering, Indian Institute of Technology, Powai, Mumbai, 400 076, India

    R. K. Pant PhD

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© 1998 Springer-Verlag London

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Bentley, P.J., Wakefield, J.P. (1998). Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds) Soft Computing in Engineering Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-1-4471-0427-8_25

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  • DOI: https://doi.org/10.1007/978-1-4471-0427-8_25

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-76214-0

  • Online ISBN: 978-1-4471-0427-8

  • eBook Packages: Springer Book Archive

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