Please use this identifier to cite or link to this item: https://doi.org/10.1021/ie101399r
DC FieldValue
dc.titleApplication of particle swarm optimization to fourier series regression of non-periodic data
dc.contributor.authorLim, E.W.C.
dc.date.accessioned2014-06-17T07:36:13Z
dc.date.available2014-06-17T07:36:13Z
dc.date.issued2011-02-16
dc.identifier.citationLim, E.W.C. (2011-02-16). Application of particle swarm optimization to fourier series regression of non-periodic data. Industrial and Engineering Chemistry Research 50 (4) : 2307-2322. ScholarBank@NUS Repository. https://doi.org/10.1021/ie101399r
dc.identifier.issn08885885
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/63502
dc.description.abstractA new methodology for solving black-box optimization problems by the continuous approach has been developed in this study. A discrete Fourier series method was derived and used for reformulation of black-box objective functions as continuous functions. Particle swarm optimization (PSO) was then applied to locate the global optimal solutions of the continuous functions derived. The continuous functions generated by the proposed discrete Fourier series method correlated almost exactly with their original black-box counterparts. The PSO algorithm was observed to be highly successful in achieving global optimization of all such objective functions considered in this study. Case studies were also carried out in which the methodology developed here was applied to the optimization of the formulation for culture media used for cultivating a freshwater microalga, Haematococcus pluvialis, as well as for a one-dimensional regression problem. Subsequently, the methodology was extended to solving a two-dimensional black-box optimization problem that was simulated based on the Rosenbrock function. The possibility of applying the same approach for solving the chemical plume tracing problem in the area of chemical defense was also successfully demonstrated. The results obtained indicate that the discrete Fourier series method coupled with the PSO algorithm is indeed a promising methodology for solving black-box optimization problems by a continuous approach. © 2010 American Chemical Society.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1021/ie101399r
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.doi10.1021/ie101399r
dc.description.sourcetitleIndustrial and Engineering Chemistry Research
dc.description.volume50
dc.description.issue4
dc.description.page2307-2322
dc.description.codenIECRE
dc.identifier.isiut000287049700060
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

3
checked on Jul 5, 2022

WEB OF SCIENCETM
Citations

2
checked on Jul 5, 2022

Page view(s)

149
checked on Jun 23, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.