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Adaptive Weighted Particle Swarm Optimisation for Multi-objective Optimal Design of Alloy Steels

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Parallel Problem Solving from Nature - PPSN VIII (PPSN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3242))

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Abstract

In this paper, a modified Particle Swarm Optimisation (PSO) algorithm is presented to improve the performance of multi-objective optimisation. The PSO algorithm search capabilities are enhanced via the inclusion of the adaptive inertia weight and acceleration factor. In addition, a weighted aggregation function has been introduced within the algorithm to guide the selection of the personal and global bests, together with a non-dominated sorting algorithm to select the particles from one iteration to another. The proposed algorithm has been successfully applied to a series of well-known benchmark functions as well as to the multi-objective optimal design of alloy steels, which aims at determining the optimal heat treatment regimes and the required weight percentages for the chemical composites in order to obtain the pre-defined mechanical properties of the material. The results have shown that the algorithm can locate the constrained optimal design with a very good accuracy

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Author information

Authors and Affiliations

  1. Institute for Microstructure and Mechanical Properties Engineering, The University of Sheffield (IMMPETUS),  

    Mahdi Mahfouf, Min-You Chen & Derek Arthur Linkens

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

    Mahdi Mahfouf, Min-You Chen & Derek Arthur Linkens

Authors
  1. Mahdi Mahfouf
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  2. Min-You Chen
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  3. Derek Arthur Linkens
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Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Xin Yao

  2. Automated Scheduling, Optimisation and Planning Group, School of Computer Science & IT, University of Nottingham, NG8 1BB, Nottingham, UK

    Edmund K. Burke

  3. Intelligent Systems Group Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizábal 1, 20018, San Sebastian, Donostia, Spain

    José A. Lozano

  4. University of the West of England, Bristol, United Kingdom

    Jim Smith

  5. GeNeura Team, Depto. Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain

    Juan Julián Merelo-Guervós

  6. School of Computer Science, The University of Birmingham, B15 1TT, Edgbaston, Birmingham, UK

    John A. Bullinaria

  7. School of Computer Science, University of Birmingham, B15 2TT, Birmingham, Great Britain

    Jonathan E. Rowe

  8. School of Computer Science, University of Birmingham, United Kingdom

    Peter Tiňo

  9. School of Computer Science, The University of Birmingham, B15 2TT, Birmingham, UK

    Ata Kabán

  10. Faculty of Computer Science, Dortmund University of Technology, 44221, Dortmund, Germany

    Hans-Paul Schwefel

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© 2004 Springer-Verlag Berlin Heidelberg

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Mahfouf, M., Chen, MY., Linkens, D.A. (2004). Adaptive Weighted Particle Swarm Optimisation for Multi-objective Optimal Design of Alloy Steels. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_77

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  • DOI: https://doi.org/10.1007/978-3-540-30217-9_77

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23092-2

  • Online ISBN: 978-3-540-30217-9

  • eBook Packages: Springer Book Archive

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