Efficient methods for finding transition states in chemical reactions: Comparison of improved dimer method and partitioned rational function optimization method

2005 ◽  
Vol 123 (22) ◽  
pp. 224101 ◽  
Author(s):  
Andreas Heyden ◽  
Alexis T. Bell ◽  
Frerich J. Keil
Keyword(s):  
Rational Function ◽  
Chemical Reactions ◽  
Transition States ◽  
Optimization Method ◽  
Function Optimization

scholarly journals Real‐time femtosecond probing of ‘‘transition states’’ in chemical reactions

1987 ◽  
Vol 87 (4) ◽  
pp. 2395-2397 ◽  
Author(s):  
Marcos Dantus ◽  
Mark J. Rosker ◽  
Ahmed H. Zewail
Keyword(s):  
Real Time ◽  
Chemical Reactions ◽  
Transition States

Rational function optimization using genetic algorithms

2007 ◽  
Vol 9 (4) ◽  
pp. 403-413 ◽  
Author(s):  
M.J. Valadan Zoej ◽  
M. Mokhtarzade ◽  
A. Mansourian ◽  
H. Ebadi ◽  
S. Sadeghian
Keyword(s):  
Genetic Algorithms ◽  
Rational Function ◽  
Function Optimization

Bifurcation of no-return transition states in many-body chemical reactions

2009 ◽  
Vol 130 (12) ◽  
pp. 124116 ◽  
Author(s):  
Chun-Biu Li ◽  
Mikito Toda ◽  
Tamiki Komatsuzaki
Keyword(s):  
Chemical Reactions ◽  
Transition States ◽  
Many Body

An Adaptive Population-based Simplex Method for Continuous Optimization

2016 ◽  
Vol 7 (4) ◽  
pp. 23-51 ◽  
Author(s):  
Mahamed G.H. Omran ◽  
Maurice Clerc
Keyword(s):  
Simplex Method ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Population Based ◽  
Function Optimization ◽  
Test Functions ◽  
Dimensional Simplex ◽  
Low Dimensional ◽  
Better Than ◽  
Real World Problems

This paper proposes a new population-based simplex method for continuous function optimization. The proposed method, called Adaptive Population-based Simplex (APS), is inspired by the Low-Dimensional Simplex Evolution (LDSE) method. LDSE is a recent optimization method, which uses the reflection and contraction steps of the Nelder-Mead Simplex method. Like LDSE, APS uses a population from which different simplexes are selected. In addition, a local search is performed using a hyper-sphere generated around the best individual in a simplex. APS is a tuning-free approach, it is easy to code and easy to understand. APS is compared with five state-of-the-art approaches on 23 functions where five of them are quasi-real-world problems. The experimental results show that APS generally performs better than the other methods on the test functions. In addition, a scalability study has been conducted and the results show that APS can work well with relatively high-dimensional problems.

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