A new asynchronous parallel global optimization method based on simulated annealing and differential evolution

2011 ◽  
Vol 11 (1) ◽  
pp. 1481-1489 ◽  
Author(s):  
Jernej Olenšek ◽  
Tadej Tuma ◽  
Janez Puhan ◽  
Árpád Bűrmen
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Differential Evolution ◽  
Optimization Method ◽  
Global Optimization Method ◽  
Asynchronous Parallel

Simulated Annealing Fitting: A Global Optimization Method for Quantitatively Analyzing Growth Kinetics of Colloidal Ag Nanoparticles

2021 ◽  
Author(s):  
Siyu Wu ◽  
Qinwei An ◽  
Yugang Sun
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Growth Kinetics ◽  
Optimization Method ◽  
Colloidal Nanoparticles ◽  
Global Optimization Method ◽  
Liquid Reaction ◽  
Solid Liquid ◽  
Kinetics Of ◽  
Heterogeneous Solid

The involvement of heterogeneous solid/liquid reaction in growing colloidal nanoparticles makes it challenging to quantitatively understand the fundamental steps that determine nanoparticles' growth kinetics. A global optimization protocol relying on...

scholarly journals What are grain boundary structures in graphene?

Nanoscale ◽  
2014 ◽  
Vol 6 (8) ◽  
pp. 4309-4315 ◽  
Author(s):  
Zheng-Lu Li ◽  
Zhi-Ming Li ◽  
Hai-Yuan Cao ◽  
Ji-Hui Yang ◽  
Qiang Shu ◽  
...  
Keyword(s):  
Global Optimization ◽  
Grain Boundary ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Interface Structure ◽  
Global Optimization Method ◽  
Evolution Algorithm

We have developed a new global optimization method for the determination of the interface structure based on the differential evolution algorithm.

PARETO SIMULATED ANNEALING (SA)-BASED MULTI-OBJECTIVE OPTIMIZATION FOR MEMS DESIGN AND APPLICATION

2005 ◽  
Vol 15 (02) ◽  
pp. 455-460 ◽  
Author(s):  
ANDOJO ONGKODJOJO ONG ◽  
FRANCIS E. H. TAY
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Optimization Method ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Global Optimization Method ◽  
Micro Electro Mechanical Systems ◽  
Pareto Ranking ◽  
Pareto Simulated Annealing ◽  
Design And Application

In this paper we present a global optimization method for multiple objective functions using the Pareto Simulated Annealing (SA). This novel optimization method is very useful and promising for design and application in the field of Micro-Electro-Mechanical Systems (MEMS). Previously published global optimization method has been reported by us for only single objective function. The proposed method automatically assigns different objective weights to each objective functions so that it can generate multiple solutions simultaneously. It also offers the trade-off between the objective functions so that we will be able to select the most suitable solution for MEMS design and applications. Based on the global Pareto ranking of the solutions, the optimization method can provide the best solution (the first Pareto ranking) as well.

Development of Global Optimization Method for Design of Turbine Stages

2005 ◽  
Author(s):  
Hiroyuki Kawagishi ◽  
Kazuhiko Kudo
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Simulated Annealing ◽  
Optimum Design ◽  
Optimization Method ◽  
Global Optimum ◽  
Global Optimization Method ◽  
Optimum Solution ◽  
Global Optimum Solution ◽  
Number Of Iterations

A new optimization method which can search for the global optimum solution and decrease the number of iterations was developed. The performance of the new method was found to be effective in finding the optimum solution for single- and multi-peaked functions for which the global optimum solution was known in advance. According to the application of the method to the optimum design of turbine stages, it was shown that the method can search the global optimum solution at approximately one seventh of the iterations of GA (Genetic Algorithm) or SA (Simulated Annealing).

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