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Fitting Lorentzian peaks with evolutionary genetic algorithm based on stochastic search procedure.
1Inonu University, Art and Science Faculty, Chemistry Department, 44280-Malatya, Turkey. mkarakaplan@inonu.edu.tr <mkarakaplan@inonu.edu.tr>
Analytica Chimica Acta
|March 28, 2007
Summary
A novel evolutionary random search method enhances curve fitting for complex data. This fast, time-saving technique accurately quantifies combined Gaussian and Lorentzian peaks, proving effective for large-scale optimization challenges.
Area of Science:
- Computational Physics
- Applied Mathematics
- Data Analysis
Background:
- Accurate peak quantification is crucial in various scientific fields.
- Existing curve fitting methods can struggle with complex, overlapping peak data and noise.
- Stochastic search techniques offer potential for robust optimization.
Purpose of the Study:
- To modify and apply a global search technique for curve fitting.
- To quantify combinations of Gaussian and Lorentzian peaks.
- To evaluate the method's suitability for complex optimization problems.
Main Methods:
- Modified evolutionary random search algorithm.
- Stochastic search procedure utilizing randomized operators (a modified Monte Carlo method).
- Testing on self-obtained overlapped Lorentzian peaks with noise, Lennard-Jones potential simulations, and benchmark mathematical functions.
Main Results:
- The developed method successfully quantified combined Gaussian and Lorentzian peaks.
- Demonstrated suitability for complex and large-scale optimization tasks.
- Outperformed or matched results from established peak fitting programs in speed and accuracy.
Conclusions:
- The modified evolutionary random search is a fast and time-saving approach for curve fitting.
- This stochastic method provides an effective solution for quantifying complex peak combinations.
- The technique is robust and applicable to diverse scientific and mathematical optimization challenges.
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