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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2011 Vol.1(1): 58-65 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.9

Continuous Stirred Tank Reactor Optimisation via Simulated Annealing, Firefly and Ant Colony Optimisation Elements on the Steepest Ascent

Pongchanun Luangpaiboon
Abstract—The objective of this work is to make use of conventional response surface methodologies and basic elements from metaheuristic algorithms in the design of influential variables for engineering systems. A method of steepest ascent and its integrated approaches with simulated annealing, firefly and ant colony optimisation algorithms, are compared on a simulated continuous stirred tank reactor or CSTR with various levels of signal noise. These metaheuristics contain the complicatedness in terms of their parameters. An additional series of computational experiments were conducted and analysed in terms of the minimax and mean squared error performance measures including Taguchi’s signal to noise ratio. Proper levels of these parameters are analysed to recommend the best parameter choices. On the experimental results of all the algorithms with the preferable levels of parameters, the method of steepest ascent seems to be the most efficient on the CSTR surface at the lower levels of noise. However, the integrated approaches with all simulated annealing, firefly and ant colony optimisation elements work well when the standard deviation of the noise is at higher levels. Although the average, the standard deviation of the greatest actual concentration of the product and percentage of sequences ended at the optimum from the integrated algorithms with simulated annealing and ant colony optimisation seem to be better, they need more average design points, especially with ant colony optimisation element, to converge to the optimum when compared.

Index Terms—Simulated annealing, firefly, ant colony optimisation; steepest ascent; continuous stirred tank reactor.

P. Luangpaiboon is an Associate Professor, the Industrial Statistics and Operational Research Unit (ISO-RU), Department of Industrial Engineering, Faculty of Engineering, Thammasat University, 12120, THAILAND. [Phone: (662)564-3002-9; Fax: (662)564-3017; e-mail: lpongch@engr.tu.ac.th].

[PDF]

Cite:Pongchanun Luangpaiboon, "Continuous Stirred Tank Reactor Optimisation via Simulated Annealing, Firefly and Ant Colony Optimisation Elements on the Steepest Ascent," International Journal of Machine Learning and Computing vol. 1, no. 1, pp. 58-65, 2011.

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