127,863 research outputs found
Generalized Simulated Annealing
We propose a new stochastic algorithm (generalized simulated annealing) for
computationally finding the global minimum of a given (not necessarily convex)
energy/cost function defined in a continuous D-dimensional space. This
algorithm recovers, as particular cases, the so called classical ("Boltzmann
machine") and fast ("Cauchy machine") simulated annealings, and can be quicker
than both. Key-words: simulated annealing; nonconvex optimization; gradient
descent; generalized statistical mechanics.Comment: 13 pages, latex, 4 figures available upon request with the authors
Deconstructing Simulated Annealing
Recent advances in mobile information and self-learning information offer a viable alternative to interrupts. Here, we disprove the refinement of the producer-consumer problem, demonstrates the practical importance of operating systems. We verify that the UNIVAC computer and simulated annealing [14] can collaborate to answer this quagmire
Variable Annealing Length and Parallelism in Simulated Annealing
In this paper, we propose: (a) a restart schedule for an adaptive simulated
annealer, and (b) parallel simulated annealing, with an adaptive and
parameter-free annealing schedule. The foundation of our approach is the
Modified Lam annealing schedule, which adaptively controls the temperature
parameter to track a theoretically ideal rate of acceptance of neighboring
states. A sequential implementation of Modified Lam simulated annealing is
almost parameter-free. However, it requires prior knowledge of the annealing
length. We eliminate this parameter using restarts, with an exponentially
increasing schedule of annealing lengths. We then extend this restart schedule
to parallel implementation, executing several Modified Lam simulated annealers
in parallel, with varying initial annealing lengths, and our proposed parallel
annealing length schedule. To validate our approach, we conduct experiments on
an NP-Hard scheduling problem with sequence-dependent setup constraints. We
compare our approach to fixed length restarts, both sequentially and in
parallel. Our results show that our approach can achieve substantial
performance gains, throughout the course of the run, demonstrating our approach
to be an effective anytime algorithm.Comment: Tenth International Symposium on Combinatorial Search, pages 2-10.
June 201
Time series forecasting using a TSK fuzzy system tuned with simulated annealing
In this paper, a combination of a Takagi-Sugeno fuzzy system (TSK) and simulated annealing is used to predict well known time series by searching for the best configuration of the fuzzy system. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the fuzzy system rules. The results of the proposed method are encouraging indicating that simulated annealing and fuzzy logic are able to combine well in time series prediction
Simulated Annealing for Topological Solitons
The search for solutions of field theories allowing for topological solitons
requires that we find the field configuration with the lowest energy in a given
sector of topological charge. The standard approach is based on the numerical
solution of the static Euler-Lagrange differential equation following from the
field energy. As an alternative, we propose to use a simulated annealing
algorithm to minimize the energy functional directly. We have applied simulated
annealing to several nonlinear classical field theories: the sine-Gordon model
in one dimension, the baby Skyrme model in two dimensions and the nuclear
Skyrme model in three dimensions. We describe in detail the implementation of
the simulated annealing algorithm, present our results and get independent
confirmation of the studies which have used standard minimization techniques.Comment: 31 pages, LaTeX, better quality pics at
http://www.phy.umist.ac.uk/~weidig/Simulated_Annealing/, updated for
publicatio
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