Many optimization techniques have been adopted for efficient job scheduling in grid computing, such as: genetic algorithms, simulated annealing and stochastic methods. Such techni...
Renato Porfirio Ishii, Rodrigo Fernandes de Mello,...
News reports are being produced and disseminated in overwhelming volume, making it difficult to keep up with the newest information. Most previous research in automatic news organ...
— This paper presents an improvement to the vanilla version of the simulated annealing algorithm by using opposite neighbors. This new technique, is based on the recently propose...
— In this work, we show that heuristic techniques (particularly Simulated Annealing) can be successfully applied in the search of good non-linear approximations of cryptographic ...
— This paper presents a single-objective and a multiobjective stochastic optimization algorithms for global training of neural networks based on simulated annealing. The algorith...
Both simulated annealing (SA) and the genetic algorithms (GA) are stochastic and derivative-free optimization technique. SA operates on one solution at a time, while the GA mainta...
Mostafa A. El-Hosseini, Aboul Ella Hassanien, Ajit...
Research into optical markerless human motion capture has attracted significant attention. However, the complexity of the human anatomy, ambiguities introduced by lacking full-pe...
We propose a sparse non-negative image coding based on simulated annealing and matrix pseudo-inversion. We show that sparsity and non-negativity are both important to obtain part-...
Abstract—In Network-on-Chip (NoC) application design, coreto-node mapping is an important but intractable optimization problem. In the paper, we use simulated annealing to tackle...
— In this paper, we propose a population-based implementation of simulated annealing to tackle multi-objective optimisation problems, in particular those of combinatorial nature....