Publication: Estimation of algae growth model parameters by a double layer genetic algorithm
Issued Date
2012-11-01
Resource Type
ISSN
22242872
11092750
11092750
Other identifier(s)
2-s2.0-84872437778
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Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
WSEAS Transactions on Computers. Vol.11, No.11 (2012), 377-386
Suggested Citation
Artorn Nokkaew, Busayamas Pimpunchat, Charin Modchang, Somkid Amornsamankul, Wannapong Triampo, Darapond Triampo Estimation of algae growth model parameters by a double layer genetic algorithm. WSEAS Transactions on Computers. Vol.11, No.11 (2012), 377-386. Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/14029
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Title
Estimation of algae growth model parameters by a double layer genetic algorithm
Abstract
This paper presents a double layer genetic algorithm (DLGA) to improve performance of the information-constrained parameter estimations. When a simple genetic algorithm (SGA) fails, a DLGA is applied to the optimization problem in which the initial condition is missing. In this study, a DLGA is specifically designed. The two layers of the SGA serve different purposes. The two optimizations are applied separately but sequentially. The first layer determines the average value of a state variable as its derivative is zero. The knowledge from the first layer is utilized to guide search in the second layer. The second layer uses the obtained average to optimize model parameters. To construct a fitness function for the second layer, the relative derivative function of the average is combined into the fitness function of the ordinary least square problem as a value control. The result shows that the DLGA has better performance. When missing an initial condition, the DLGA provides more consistent numerical values for model parameters. Also, simulation produced by DLGA is more reasonable values than those produced by the SGA.