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A modified PSO with a dynamically varying population and its application to the multi-objective optimal design of alloy steels

Zhang, Qian and Mahfouf, Mahdi (2009) A modified PSO with a dynamically varying population and its application to the multi-objective optimal design of alloy steels. In: 2009 IEEE Congress on Evolutionary Computation. IEEE, pp. 3241-3248. ISBN 978-1-4244-2958-5. (doi:10.1109/CEC.2009.4983355) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:50552)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://doi.org/10.1109/CEC.2009.4983355

Abstract

In this paper, a new mechanism for dynamically varying the population size is proposed based on a previously modified PSO algorithm (nPSO). This new algorithm is extended to the multi-objective optimisation case by applying the Random Weighted Aggregation (RWA) technique and by maintaining an archive for preserving the suitable Pareto-optimal solutions. Both the single objective and multi-objective optimisation algorithms were tested using well-known benchmark problems. The results show that the proposed algorithms outperform some of the other salient Evolutionary Algorithms (EAs). The proposed algorithms were further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of dasiaright-first-time productionpsila of metals.

Item Type: Book section
DOI/Identification number: 10.1109/CEC.2009.4983355
Uncontrolled keywords: iron alloys; steel; benchmark testing; evolutionary computation; algorithm design and analysis; heat treatment; chemical products; mechanical factors; production; cost function
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering
T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 16:25 UTC
Last Modified: 16 Nov 2021 10:21 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50552 (The current URI for this page, for reference purposes)

University of Kent Author Information

Zhang, Qian.

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