simulated annealing


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simulated annealing

A technique which can be applied to any minimisation or learning process based on successive update steps (either random or deterministic) where the update step length is proportional to an arbitrarily set parameter which can play the role of a temperature. Then, in analogy with the annealing of metals, the temperature is made high in the early stages of the process for faster minimisation or learning, then is reduced for greater stability.
References in periodicals archive ?
La presente investigacion se muestra varias heuristicas y desarrolla la comparacion de simulaciones hasta los 180 Erlangs en la red NSFNET (National Science Foundation Network), mostrando una nueva meta heuristicas basada en el algoritmo Snake One y muestra los resultados para la meta heuristica Snake Three (SNK3), comparada con las heuristicas algoritmo genetico (AG), Simulated Annealing (SA), algoritmo de referencia, Tabu Search (TS), Snake One (SNK1), Snake Two (SNK2) (Zang et al.
Furthermore, ANN and simulated annealing (SA), combined with other optimized algorithms, can perfectly replace the traditional traffic flow forecasting model [33].
Sahingoz, "GPU-Accelerated Flight Route Planning for Multi-UAV Systems Using Simulated Annealing," in International Conference on Artificial Intelligence: Methodology, Systems, and Applications, 2016, pp.
In this work, Stochastics Beam Search (SBS) , Simulated Annealing (SA), Fractional Open Circuit Voltage (FOCV), and Stochastic Hill Climbing (SHC) algorithms have been compared.
Augmenting simulated annealing to build interaction test suites, in: Proc.
The simulated annealing algorithm is a heuristic random search algorithm based on Monte Carlo iterative algorithm, algorithm proposed by the N Metropolis et al.
used an arbitrarily set weighting factor in the cost function in the simulated annealing procedure.
In order to further improve searching ability of the algorithm, the orthogonal design and simulated annealing operation are integrated into the CS algorithm.
The upper-level is to find an optimal lane-based reversal plan by a tabu search algorithm; the lower-level is to get two types of lane-based route plans with crossing conflict elimination utilizing a simulated annealing algorithm.
Since the evolvement of Genetic Algorithm (GA) [1] and Simulated Annealing (SA) [2] type algorithms the idea of adaptation of natural phenomena has become an effective source of developing efficient and potential optimization techniques.
GenSA: Provides R functions for generalized simulated annealing algorithm.
Practical considerations in the application of simulated annealing to stochastic simulation.