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Genetic Methods in Multi-objective Optimization of Structures with an Equality Constraint on Volume

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Evolutionary Multi-Criterion Optimization (EMO 2003)

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

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Abstract

The method is developed for multi-objective optimization problems. Its purpose is to evolve an evenly distributed group of solutions to determine the optimum Pareto set for a given problem. The algorithm determines a set of solutions (a population), this population being sorted by its domination properties and a filter is defined in order to retain the Pareto solutions.

In most topology design problem volume is in general a constraint of the problem. Due to this constraint, all chromosomes used in the genetic algorithm must generate individuals with the same volume value; in the coding adopted this means that they must preserve the same number of ones and, implicitly, the same number of zeros, along the evolutionary process. It is thus necessary to define these chromosomes and to create corresponding operators of crossover and mutation which preserve volume.

To reduce computational effort, optimal solutions of each of the single-objective problems are introduced in the initial population.

Results obtained by the evolutionary and classical methods are compared.

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Author information

Authors and Affiliations

  1. IDMEC/IST-Mechanical Engineering Department, Instituto Superior Técnico, Av. Rovisco Pais, 1049-001, Lisbon, Portugal

    J. F. Aguilar Madeira, H. Rodrigues & Heitor Pina

  2. ISEL-Instituto Superior de Engenharia de Lisboa, Portugal

    J. F. Aguilar Madeira

Authors
  1. J. F. Aguilar Madeira
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  2. H. Rodrigues
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  3. Heitor Pina
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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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Aguilar Madeira, J.F., Rodrigues, H., Pina, H. (2003). Genetic Methods in Multi-objective Optimization of Structures with an Equality Constraint on Volume. 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_54

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  • DOI: https://doi.org/10.1007/3-540-36970-8_54

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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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