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Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.ORCID iD: 0000-0003-0111-1776
De Montfort University.
De Montfort University.
2010 (English)In: European Journal of Operational Research, ISSN 0377-2217, Vol. 204, no 3, 533-544 p.Article in journal (Refereed) Published
Abstract [en]

Many real-world optimisation problems approached by evolutionary algorithms are subject to noise. When noise is present, the evolutionary selection process may become unstable and the convergence of the optimisation adversely affected. In this paper, we present a new technique that efficiently deals with noise in multi-objective optimisation. This technique aims at preventing the propagation of inferior solutions in the evolutionary selection due to noisy objective values. This is done by using an iterative resampling procedure that reduces the noise until the likelihood of selecting the correct solution reaches a given confidence level. To achieve an efficient utilisation of resources, the number of samples used per solution varies based on the amount of noise in the present area of the search space. The proposed algorithm is evaluated on the ZDT benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of engine component manufacturing in aviation industry, while the second real-world problem concerns the optimisation of a camshaft machining line in automotive industry. The results from the optimisations indicate that the proposed technique is successful in reducing noise, and it competes successfully with other noise handling techniques.

Place, publisher, year, edition, pages
Elsevier, 2010. Vol. 204, no 3, 533-544 p.
Keyword [en]
Evolutionary computations, Multi-objective optimisation, Noise, Simulation
National Category
Engineering and Technology
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-3901DOI: 10.1016/j.ejor.2009.11.003ISI: 000275241100015Scopus ID: 2-s2.0-75149166405OAI: oai:DiVA.org:his-3901DiVA: diva2:318133
Available from: 2010-05-06 Created: 2010-05-06 Last updated: 2015-12-18Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
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More styles
Language
  • de-DE
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  • Other locale
More languages
Output format
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