Minimization of Molecular Potential Energy Function Using newly developed Real Coded Genetic Algorithms

Authors

  • Kusum DEEP Indian Institute of Technology Roorkee
  • Shashi BARAK Indian Institute of Technology Roorkee
  • Vinod Kumar KATIYAR Indian Institute of Technology Roorkee
  • Atulya Kumar NAGAR Liverpool Hope University, Liverpool, U.K.

DOI:

https://doi.org/10.11121/ijocta.01.2012.0044

Abstract

The problem of finding the global minimum of molecular potential energy function is very challenging for algorithms which attempt to determine global optimal solution. The principal difficulty in minimizing the molecular potential energy function is that the number of local minima increases exponentially with the size of the molecule. The global minimum of the potential energy of a molecule corresponds to its most stable conformation, which dictates majority of its properties. In this paper the efficiency of four newly developed real coded genetic algorithms is tested on the molecular potential energy function. The minimization of the function is performed on an independent set of internal coordinates involving only torsion angles. Computational results with up to 100 degrees of freedom are presented.

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Author Biographies

Kusum DEEP, Indian Institute of Technology Roorkee

Department of Mathematics 

Associate Professor

Shashi BARAK, Indian Institute of Technology Roorkee

Department of Mathematics

Research Scholar

Vinod Kumar KATIYAR, Indian Institute of Technology Roorkee

Department of Mathematics

Professor

Atulya Kumar NAGAR, Liverpool Hope University, Liverpool, U.K.

Centre for Applicable Mathematics and Systems Science (CAMSS), Department of Computer and Mathematical Sciences

Professor

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Published

2011-12-16
CITATION
DOI: 10.11121/ijocta.01.2012.0044
Published: 2011-12-16

How to Cite

DEEP, K., BARAK, S., KATIYAR, V. K., & NAGAR, A. K. (2011). Minimization of Molecular Potential Energy Function Using newly developed Real Coded Genetic Algorithms. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 2(1), 51–58. https://doi.org/10.11121/ijocta.01.2012.0044

Issue

Section

Optimization & Applications