Journal Article10.1002/RSA.3240030204
Parallel simulated annealing
31
TL;DR: The convergence of the annealing algorithm in the restricted parallel form is established, for an arbitrary network, a generalization of the unlimited parallelism for Boltzmann machines.
read more
Abstract: This article introduces the notion of restricted parallelism for networks, a generalization of the unlimited parallelism for Boltzmann machines. The convergence of the annealing algorithm in the restricted parallel form is established, for an arbitrary network. © 1992 Wiley Periodicals, Inc.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Metaheuristics: A bibliography
Ibrahim H. Osman,Gilbert Laporte +1 more
TL;DR: This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics that have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas.
740
The Theory and Practice of Simulated Annealing
Darrall Henderson,Sheldon H. Jacobson,Alan W. Johnson +2 more
- 01 Jan 2003
TL;DR: This chapter presents practical guidelines for the implementation of simulated annealing in terms of cooling schedules, neighborhood functions, and appropriate applications, as well as recent advances in the analysis of finite time performance.
580
Generalized Response Surface Model Updating Using Time Domain Data
TL;DR: A procedure to design and fit proper RS models in finite-element model updating problems and formulation of the problem in an iterative format in time domain is proposed to extract more information from measured signals and compensate for the error present in the regressed models.
46
Analysis of static simulated annealing algorithms
J. E. Orosz,Sheldon H. Jacobson +1 more
TL;DR: A measure for determining the expected number of iterations to visit a predetermined objective functionlevel, given that an inferior objective function level has been reached in a finite number of iteration, is introduced.
39
Parallel computational optimization in operations research: A new integrative framework, literature review and research directions
TL;DR: A new integrative framework of parallel computational optimization across optimization problems, algorithms and application domains is suggested and applied to synthesize prior research on parallel optimization in OR, focusing on computational studies published in the period 2008-2017.
38
References
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Cooling Schedules for Optimal Annealing
TL;DR: A Monte Carlo optimization technique called “simulated annealing” is a descent algorithm modified by random ascent moves in order to escape local minima which are not global minima.