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

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Summary

Introduction

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.

Method
ADE speed-up for parallel computation
Findings
Conclusions
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