Abstract
In this work we use an Asynchronous Differential Evolution (ADE) method to estimate parameters of the Separated Form Factor (SFF) model which is used to investigate a structure of drug delivery Phospholipid Transport Nano System (PTNS) unilamellar vesicles by experimental small angle synchrotron X-ray scattering spectra (SAXS). We compare the efficiency of different optimizing procedures (OP) for the search for the SFF-model parameters. It is shown that the probability to find the global solution of this problem by ADE-methods is significantly higher than that by either Nelder-Mead method or a Quasi-Newton method with Davidon-Fletcher-Powell formula. The parallel realization of ADE accelerates the calculations significantly. The speed-up obtained by the parallel realization of ADE and results of the model are presented.
Highlights
Differential Evolution (DE) [1] is an efficient algorithm to solve global optimization problems
In this work we use an Asynchronous Differential Evolution (ADE) method to estimate parameters of the Separated Form Factor (SFF) model which is used to investigate a structure of drug delivery Phospholipid Transport Nano System (PTNS) unilamellar vesicles by experimental small angle synchrotron X-ray scattering spectra (SAXS)
The parameters of a polydispersed population of unilamellar vesicles PTNS, including the internal structure of the lipid bilayer of vesicles membrane have been estimated by the ADE-method
Summary
Differential Evolution (DE) [1] is an efficient algorithm to solve global optimization problems. All the tested strategies of ADE converged to the global minimum with a probability of more than 70%, and the most successful (DE/rand/rand/1/acm) — with a probability of more than 90% Note that this probability could be further enhanced by increasing the maximum number of function evaluations allowed. The use of “fast track” strategy DE/linworst/current-to-pbest/1/acm reduces the average number of function evaluations approximately three times, but this strategy has a larger probability of ending calculations in one of the local minima. ADE and ADE-ACM are robust global minimum search methods They end the calculations with a larger probability at the minimum of the problem, while the search using SIMPLEX and MIGRAD ended at the minimum with a chance of 1±1% and 43±7%, respectively.
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