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: Evolutionary Computation, 2009. CEC '09. IEEE Congress on. IEEE pp. 3241-3248. ISBN 978-1-4244-2958-5. E-ISBN 978-1-4244-2959-2. (doi:https://doi.org/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)

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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: Conference or workshop item (Paper)
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
T Technology > TA Engineering (General). Civil engineering (General) > TA 403 Materials Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 16:25 UTC
Last Modified: 21 Sep 2015 14:20 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50552 (The current URI for this page, for reference purposes)
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