Automated spectroscopic analysis using the particle swarm optimization algorithm: Implementing a guided search algorithm to AUTOFIT

Ervin, Katherine

Permalink

https://hdl.handle.net/2142/97082 Copy

Description

Title

Automated spectroscopic analysis using the particle swarm optimization algorithm: Implementing a guided search algorithm to AUTOFIT

Author(s)

Ervin, Katherine

Contributor(s)

Shipman, Steven

Issue Date

2017-06-19

Keyword(s)

Comparing theory and experiment

Date of Ingest

• 2018-01-29T23:05:38Z
• 2017-07-27T20:15:41Z

Abstract

While rotational spectra can be rapidly collected, their analysis (especially for complex systems) is seldom straightforward, leading to a bottleneck. The AUTOFIT programfootnote{Seifert, N.A., Finneran, I.A., Perez, C., Zaleski, D.P., Neill, J.L., Steber, A.L., Suenram, R.D., Lesarri, A., Shipman, S.T., Pate, B.H., J. Mol. Spec. 312, 13-21 (2015)} was designed to serve that need by quickly matching rotational constants to spectra with little user input and supervision. This program can potentially be improved by incorporating an optimization algorithm in the search for a solution. The Particle Swarm Optimization Algorithm (PSO) was chosen for implementation. PSO is part of a family of optimization algorithms called heuristic algorithms, which seek approximate best answers. This is ideal for rotational spectra, where an exact match will not be found without incorporating distortion constants, etc., which would otherwise greatly increase the size of the search space. PSO was tested for robustness against five standard fitness functions and then applied to a custom fitness function created for rotational spectra. This talk will explain the Particle Swarm Optimization algorithm and how it works, describe how Autofit was modified to use PSO, discuss the fitness function developed to work with spectroscopic data, and show our current results.

Publisher

International Symposium on Molecular Spectroscopy

Type of Resource

text

Genre of Resource

Conference Paper / Presentation

Language

eng

Permalink

http://hdl.handle.net/2142/97082

DOI

https://doi.org/10.15278/isms.2017.MI04

Copyright and License Information

Copyright 2017 Katherine Ervin

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