An Application of Nelder-Mead Heuristic-based Hybrid Algorithms: Estimation of Compartment Model Parameters
Compartment models are commonly used tools for nonlinear modeling in pharmacokinetic studies. Parameter estimation of compartment models play a crucial role in drug development. In order to estimate the model parameters, a derivative-based method, called stripping, has been commonly used in drug studies until now. In this study, a derivative free simple local search algorithm, Nelder-Mead Simplex (NMS), is hybridized with two artificial intelligence optimization algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The hybridized algorithms are called GANMS and PSONMS which are used for parameter estimation. These hybrid algorithms are all population based and do not need any assumptions which make the calculations become easier. Two data sets with two compartment models are preferred as application from the literature. It is seen from the results that the suggested PSONMS is more preferable among the GA, PSO and GANMS with consistence parameter estimates and small error function values.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.