00000caa a22000003i 4500 UP-1685594773862408676 Buklod 20210407212559.0 m | | ta 210407s2020 xx r |||| u|eng d (iLib)UPBAG-00039679837 STII-DOST BAG rda eng ARTICLE-3006 Cajayon, Raquel C. author. Comparison of two nature-inspired algorithms for parameter estimation of S-system models by Raquel C. Cajayon, Jayson A. Lucilo, Carlene PC. Pilar-Arceo, and Eduardo R. Mendoza. 2020. pages 55-70 color illustration 26 cm text rdacontent unmediated rdamedia volume rdacarrier Includes bibliographical references (pages 69-70) Parameter estimation for models of biochemical systems is computationally expensive due to the nonlinearity and high dimensionality of the coupled systems of ordinary differential equations underlying the models. Hence, it is important to apply novel methods to the problem and evaluate their performance. We consider the Bat algorithm (BA) and the Firefly algorithm (FA) with respect to parameter estimation of S-system models. Using three S-systems of increasing complexity from the MADMan benchmarking framework, we assess and compare the relative performance of the two algorithms relative to various data sets, initial conditions, and noise levels. Simulation results show that both algorithms can be effectively used in estimating parameters of the S-system models. In particular, in all three S-systems used, the FA performed better than the BA based on the final cost function values and relative estimate errors. The introduction of noise to data significantly affected the convergence of both algorithms. (Author's abstract) Mathematics. Bat algorithm. Biochemical systems. Firefly algorithm. Nature-inspired algorithm. Parameter estimation. S-system model. Mendoza, Eduardo R. author. Lucilo, Jayson A. author. Pilar-Arceo, Carlene PC. author. The Philippine Journal of Science Vol. 149, no. 1, March 2020. Request full-text access via UPB University Library through https://forms.gle/KZjBv7aRtY6jiL5E9 (viewed 07 April 2021) FI UPBAG UPBAG-MAIN ARTICLE-3006 Analytics