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

  • Mahdi Mahfouf
  • Min-You Chen
  • Derek Arthur Linkens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Mahdi Mahfouf
    • 1
    • 2
  • Min-You Chen
    • 1
    • 2
  • Derek Arthur Linkens
    • 1
    • 2
  1. 1.Institute for Microstructure and Mechanical Properties EngineeringThe University of Sheffield (IMMPETUS) 
  2. 2.Department of Automatic Control and Systems EngineeringThe University of SheffieldSheffieldUK

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