Convergence of a global stochastic optimization algorithm with partial step size restarting

2000 ◽  
Vol 32 (02) ◽  
pp. 480-498
Author(s):  
G. Yin
Keyword(s):  
Monte Carlo ◽  
Global Optimization ◽  
Simulated Annealing ◽  
Weak Convergence ◽  
Stochastic Optimization ◽  
Optimization Algorithm ◽  
Step Size ◽  
Wide Range ◽  
Stochastic Optimization Algorithm ◽  
Annealing Algorithms

This work develops a class of stochastic global optimization algorithms that are Kiefer-Wolfowitz (KW) type procedures with an added perturbing noise and partial step size restarting. The motivation stems from the use of KW-type procedures and Monte Carlo versions of simulated annealing algorithms in a wide range of applications. Using weak convergence approaches, our effort is directed to proving the convergence of the underlying algorithms under general noise processes.

Convergence of a global stochastic optimization algorithm with partial step size restarting

2000 ◽  
Vol 32 (2) ◽  
pp. 480-498 ◽  
Author(s):  
G. Yin
Keyword(s):  
Monte Carlo ◽  
Global Optimization ◽  
Simulated Annealing ◽  
Weak Convergence ◽  
Stochastic Optimization ◽  
Optimization Algorithm ◽  
Step Size ◽  
Wide Range ◽  
Stochastic Optimization Algorithm ◽  
Annealing Algorithms

This work develops a class of stochastic global optimization algorithms that are Kiefer-Wolfowitz (KW) type procedures with an added perturbing noise and partial step size restarting. The motivation stems from the use of KW-type procedures and Monte Carlo versions of simulated annealing algorithms in a wide range of applications. Using weak convergence approaches, our effort is directed to proving the convergence of the underlying algorithms under general noise processes.

Geometric Optimization of Concentrating Solar Collectors using Monte Carlo Simulation

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
A. J. Marston ◽  
K. J. Daun ◽  
M. R. Collins
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stochastic Programming ◽  
Objective Function ◽  
Sample Size ◽  
Optimization Algorithm ◽  
Geometric Optimization ◽  
Trial And Error ◽  
Solar Collectors ◽  
Step Size

This paper presents an optimization algorithm for designing linear concentrating solar collectors using stochastic programming. A Monte Carlo technique is used to quantify the performance of the collector design in terms of an objective function, which is then minimized using a modified Kiefer–Wolfowitz algorithm that uses sample size and step size controls. This process is more efficient than traditional “trial-and-error” methods and can be applied more generally than techniques based on geometric optics. The method is validated through application to the design of three different configurations of linear concentrating collector.

Parallel MCMC methods for global optimization

2019 ◽  
Vol 25 (3) ◽  
pp. 227-237
Author(s):  
Lihao Zhang ◽  
Zeyang Ye ◽  
Yuefan Deng
Keyword(s):  
Monte Carlo ◽  
Global Optimization ◽  
Statistical Theory ◽  
Simulated Annealing ◽  
Parallel Computers ◽  
Mcmc Methods ◽  
Benchmark Function ◽  
Mcmc Method ◽  
Validation And Verification ◽  
Parallel Scheme

Abstract We introduce a parallel scheme for simulated annealing, a widely used Markov chain Monte Carlo (MCMC) method for optimization. Our method is constructed and analyzed under the classical framework of MCMC. The benchmark function for optimization is used for validation and verification of the parallel scheme. The experimental results, along with the proof based on statistical theory, provide us with insights into the mechanics of the parallelization of simulated annealing for high parallel efficiency or scalability for large parallel computers.

scholarly journals PID2018 Benchmark Challenge: Multi-Objective Stochastic Optimization Algorithm

2018 ◽  
Vol 51 (4) ◽  
pp. 877-881 ◽  
Author(s):  
Abdullah Ates ◽  
Jie Yuan ◽  
Sina Dehghan ◽  
Yang Zhao ◽  
Celaleddin Yeroglu ◽  
...  
Keyword(s):  
Stochastic Optimization ◽  
Optimization Algorithm ◽  
Multi Objective ◽  
Stochastic Optimization Algorithm
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