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Multi-level Multi-objective Genetic Algorithm Using Entropy to Preserve Diversity

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Book cover Evolutionary Multi-Criterion Optimization (EMO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2632))

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Abstract

We present a new method for solving a multi-level multi-objective optimization problem that is hierarchically decomposed into several sub-problems. The method preserves diversity of Pareto solutions by maximizing an entropy metric, a quantitative measure of distribution quality of a set of solutions. The main idea behind the method is to optimize the sub-problems independently using a Multi-Objective Genetic Algorithm (MOGA) while systematically using the entropy values of intermediate solutions to guide the optimization of sub-problems to the overall Pareto solutions. As a demonstration, we applied the multi-level MOGA to a mechanical design example: the design of a speed reducer. We also solved the example in its equivalent single-level form by a MOGA. The results show that our proposed multi-level multi-objective optimization method obtains more Pareto solutions with a better diversity compared to those obtained by the single-level MOGA.

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Author information

Authors and Affiliations

  1. Departmentof Mechanical Engineering, University of Maryland, College Park, Maryland, 20742, USA

    S. Gunawan, A. Farhang-Mehr & S. Azarm

Authors
  1. S. Gunawan
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  2. A. Farhang-Mehr
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  3. S. Azarm
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Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, Centre for Intelligent Systems, University of the Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Carlos M. Fonseca

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Peter J. Fleming

  3. Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Gloriastraße 35, 8092, Zürich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  4. Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, UP, 208 016, India

    Kalyanmoy Deb

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© 2003 Springer-Verlag Berlin Heidelberg

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Gunawan, S., Farhang-Mehr, A., Azarm, S. (2003). Multi-level Multi-objective Genetic Algorithm Using Entropy to Preserve Diversity. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_11

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  • DOI: https://doi.org/10.1007/3-540-36970-8_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01869-8

  • Online ISBN: 978-3-540-36970-7

  • eBook Packages: Springer Book Archive

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