Topic
Simulated annealing
About: Simulated annealing is a research topic. Over the lifetime, 21436 publications have been published within this topic receiving 563467 citations.
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TL;DR: In this article, the authors introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. But quantum fluctuations cause transitions between states and thus play the same role as thermal fluctuations in the conventional approach.
Abstract: We introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. Quantum fluctuations cause transitions between states and thus play the same role as thermal fluctuations in the conventional approach. The idea is tested by the transverse Ising model, in which the transverse field is a function of time similar to the temperature in the conventional method. The goal is to find the ground state of the diagonal part of the Hamiltonian with high accuracy as quickly as possible. We have solved the time-dependent Schr\"odinger equation numerically for small size systems with various exchange interactions. Comparison with the results of the corresponding classical (thermal) method reveals that the quantum annealing leads to the ground state with much larger probability in almost all cases if we use the same annealing schedule.
1,710 citations
TL;DR: This implementation of simulated annealing was used in "Global Optimization of Statistical Functions with Simulated Annealing," Goffe, Ferrier and Rogers, Journal of Econometrics, vol.
1,665 citations
TL;DR: A new global optimization algorithm for functions of continuous variables is presented, derived from the “Simulated Annealing” algorithm recently introduced in combinatorial optimization, which is quite costly in terms of function evaluations, but its cost can be predicted in advance, depending only slightly on the starting point.
Abstract: A new global optimization algorithm for functions of continuous variables is presented, derived from the “Simulated Annealing” algorithm recently introduced in combinatorial optimization.The algorithm is essentially an iterative random search procedure with adaptive moves along the coordinate directions. It permits uphill moves under the control of a probabilistic criterion, thus tending to avoid the first local minima encountered.The algorithm has been tested against the Nelder and Mead simplex method and against a version of Adaptive Random Search. The test functions were Rosenbrock valleys and multiminima functions in 2,4, and 10 dimensions.The new method proved to be more reliable than the others, being always able to find the optimum, or at least a point very close to it. It is quite costly in term of function evaluations, but its cost can be predicted in advance, depending only slightly on the starting point.
1,598 citations
Book•
01 Oct 1999
TL;DR: The techniques treated in this text represent research as elucidated by the leaders in the field and are applied to real problems, such as hilllclimbing, simulated annealing, and tabu search.
Abstract: Optimization is a pivotal aspect of software design. The techniques treated in this text represent research as elucidated by the leaders in the field. The optimization methods are applied to real problems, such as hilllclimbing, simulated annealing, and tabu search.
1,461 citations
TL;DR: The effects of multiple sequence information and different types of conformational constraints on the overall performance of the method are investigated, and the ability of a variety of recently developed scoring functions to recognize the native-like conformations in the ensembles of simulated structures are investigated.
1,437 citations