scholarly journals A global optimization perspective on molecular clusters

2017 ◽  
Vol 375 (2092) ◽  
pp. 20160198 ◽  
Author(s):  
J. M. C. Marques ◽  
F. B. Pereira ◽  
J. L. Llanio-Trujillo ◽  
P. E. Abreu ◽  
M. Albertí ◽  
...  
Keyword(s):  
Global Optimization ◽  
Gas Phase ◽  
Chemical Structure ◽  
Optimization Methods ◽  
Molecular Clusters ◽  
Optimization Techniques ◽  
Stationary Points ◽  
Statistical Dynamics ◽  
Theoretical View ◽  
Global Optimization Methods

Although there is a long history behind the idea of chemical structure, this is a key concept that continues to challenge chemists. Chemical structure is fundamental to understanding most of the properties of matter and its knowledge for complex systems requires the use of state-of-the-art techniques, either experimental or theoretical. From the theoretical view point, one needs to establish the interaction potential among the atoms or molecules of the system, which contains all the information regarding the energy landscape, and employ optimization algorithms to discover the relevant stationary points. In particular, global optimization methods are of major importance to search for the low-energy structures of molecular aggregates. We review the application of global optimization techniques to several molecular clusters; some new results are also reported. Emphasis is given to evolutionary algorithms and their application in the study of the microsolvation of alkali-metal and Ca 2+ ions with various types of solvents. This article is part of the themed issue ‘Theoretical and computational studies of non-equilibrium and non-statistical dynamics in the gas phase, in the condensed phase and at interfaces’.

scholarly journals Calibration of the modified Bartlett-Lewis model using global optimization techniques and alternative objective functions

2012 ◽  
Vol 16 (3) ◽  
pp. 873-891 ◽  
Author(s):  
W. J. Vanhaute ◽  
S. Vandenberghe ◽  
K. Scheerlinck ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Global Optimization ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Objective Functions ◽  
Local Search Algorithms ◽  
Stochastic Point Process ◽  
Shuffled Complex Evolution ◽  
Global Optimization Methods ◽  
Downhill Simplex

Abstract. The calibration of stochastic point process rainfall models, such as of the Bartlett-Lewis type, suffers from the presence of multiple local minima which local search algorithms usually fail to avoid. To meet this shortcoming, four relatively new global optimization methods are presented and tested for their ability to calibrate the Modified Bartlett-Lewis Model. The list of tested methods consists of: the Downhill Simplex Method, Simplex-Simulated Annealing, Particle Swarm Optimization and Shuffled Complex Evolution. The parameters of these algorithms are first optimized to ensure optimal performance, after which they are used for calibration of the Modified Bartlett-Lewis model. Furthermore, this paper addresses the choice of weights in the objective function. Three alternative weighing methods are compared to determine whether or not simulation results (obtained after calibration with the best optimization method) are influenced by the choice of weights.

Evaluation of global optimization methods for conceptual rainfall-runoff model calibration

1997 ◽  
Vol 36 (5) ◽  
pp. 53-60 ◽  
Author(s):  
V. A. Cooper ◽  
V. T. V. Nguyen ◽  
J. A. Nicell
Keyword(s):  
Global Optimization ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Global Optimum ◽  
Hydrologic Model ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Global Optimization Methods ◽  
Optimization Search

The calibration of conceptual rainfall runoff (CRR) models is an optimization problem whose objective is to determine the values of the model parameters which provide the best fit between observed and estimated flows. This study investigated the performance of three probabilistic optimization techniques for calibrating the Tank model, a hydrologic model typical of CRR models. These methods were the Shuffled Complex Evolution (SCE), genetic algorithms (GA) and simulated annealing (SA) methods. It was found that performances depended on the choice of the objective function considered and also an the position of the start of the optimization search relative to the global optimum. Of the three global optimization methods (GOM) in the study, the SCE method provided better estimates of the optimal solution than the GA and SA methods. Regarding the efficiency of the GOMs, as expressed by the number of iterations for convergence, the ranking in order of decreasing performance was the SCE, the GA and the SA methods.

AN ASSESSMENT OF THE PERFORMANCE OF GLOBAL OPTIMIZATION METHODS FOR GEO-ACOUSTIC INVERSION

2008 ◽  
Vol 16 (02) ◽  
pp. 199-223 ◽  
Author(s):  
MIRJAM SNELLEN ◽  
DICK G. SIMONS
Keyword(s):  
Global Optimization ◽  
Optimization Methods ◽  
Simplex Algorithm ◽  
Optimization Techniques ◽  
Acoustic Properties ◽  
Superior Performance ◽  
Efficient Global Optimization ◽  
Acoustic Inversion ◽  
Global Optimization Methods ◽  
Downhill Simplex

Having available efficient global optimization methods is of high importance when going to a practical application of geo-acoustic inversion, where fast processing of the data is an essential requirement. A series of global optimization techniques are available and have been described in literature. In this paper three optimization techniques are considered, being a genetic algorithm (GA), differential evolution (DE), and the downhill simplex algorithm (DHS). The performance of these three methods is assessed using a test function, demonstrating superior performance of DE. Additionally, the DE optimal setting is determined. As a next step DE is applied for determining the geo-acoustic properties of the upper seabed sediments from simulated seabed reflection loss, indicating good DE performance also for real geo-acoustic inversion problems.

scholarly journals Calibration of the modified Bartlett-Lewis model using global optimization techniques and alternative objective functions

2011 ◽  
Vol 8 (6) ◽  
pp. 9707-9756 ◽  
Author(s):  
W. J. Vanhaute ◽  
S. Vandenberghe ◽  
K. Scheerlinck ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Global Optimization ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Rainfall Time Series ◽  
Stochastic Point Process ◽  
Shuffled Complex Evolution ◽  
Global Optimization Methods ◽  
Downhill Simplex

Abstract. The use of rainfall time series for various applications is widespread. However, in many cases historical rainfall records lack in length or quality for certain practical purposes, resulting in a reliance on rainfall models to supply simulated rainfall time series, e.g., in the design of hydraulic structures. One way to obtain such simulations is by means of stochastic point process rainfall models, such as the Bartlett-Lewis type of model. It is widely acknowledged that the calibration of such models suffers from the presence of multiple local minima which local search algorithms usually fail to avoid. To meet this shortcoming, four relatively new global optimization methods are presented and tested for their abilities to calibrate the Modified Bartlett-Lewis Model (MBL). The list of tested methods consists of: the Downhill Simplex Method (DSM), Simplex-Simulated Annealing (SIMPSA), Particle Swarm Optimization (PSO) and Shuffled Complex Evolution (SCE-UA). The parameters of these algorithms are first optimized to ensure optimal performance, after which they are used for calibration of the MBL model. Furthermore, this paper addresses the issue of subjectivity in the choice of weights in the objective function. Three alternative weighing methods are compared to determine whether or not simulation results (obtained after calibration with the best optimization method) are influenced by the choice of weights.

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