Convergence properties of simulated annealing for continuous global optimization

1996 ◽  
Vol 33 (4) ◽  
pp. 1127-1140 ◽  
Author(s):  
M. Locatelli
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Probability Distribution ◽  
Convergence Properties ◽  
Convergence Of Algorithms ◽  
Cooling Schedules ◽  
The Subject ◽  
Continuous Global Optimization ◽  
Annealing Algorithms ◽  
Candidate Point

In this paper conditions for the convergence of a class of simulated annealing algorithms for continuous global optimization are given. The previous literature about the subject gives results for the convergence of algorithms in which the next candidate point is generated according to a probability distribution whose support is the whole feasible set. A class of possible cooling schedules has been introduced in order to remove this restriction.

Convergence properties of simulated annealing for continuous global optimization

1996 ◽  
Vol 33 (04) ◽  
pp. 1127-1140 ◽  
Author(s):  
M. Locatelli
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Probability Distribution ◽  
Convergence Properties ◽  
Convergence Of Algorithms ◽  
Cooling Schedules ◽  
The Subject ◽  
Continuous Global Optimization ◽  
Annealing Algorithms ◽  
Candidate Point

In this paper conditions for the convergence of a class of simulated annealing algorithms for continuous global optimization are given. The previous literature about the subject gives results for the convergence of algorithms in which the next candidate point is generated according to a probability distribution whose support is the whole feasible set. A class of possible cooling schedules has been introduced in order to remove this restriction.

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.

TUNING STRATEGIES IN CONSTRAINED SIMULATED ANNEALING FOR NONLINEAR GLOBAL OPTIMIZATION

2000 ◽  
Vol 09 (01) ◽  
pp. 3-25 ◽  
Author(s):  
BENJAMIN W. WAH ◽  
TAO WANG
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Mixed Integer ◽  
Asymptotic Convergence ◽  
Necessary And Sufficient Condition ◽  
Global Minima ◽  
Global Optimization Algorithm ◽  
Constrained Simulated Annealing ◽  
Cooling Schedules ◽  
Necessary And Sufficient

This paper studies various strategies in constrained simulated annealing (CSA), a global optimization algorithm that achieves asymptotic convergence to constrained global minima (CGM) with probability one for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary and sufficient condition for discrete constrained local minima (CLM) in the theory of discrete Lagrange multipliers and its extensions to continuous and mixed-integer constrained NLPs. The strategies studied include adaptive neighborhoods, distributions to control sampling, acceptance probabilities, and cooling schedules. We report much better solutions than the best-known solutions in the literature on two sets of continuous benchmarks and their discretized versions.

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