Simulated Annealing (SA) is a powerful stochastic search method that can produce very high quality solutions for hard combinatorial optimization problem. In this paper, we applied this SA method to optimize our 3D hierarchical reconstruction neural network (NN). This NN deals with complicated task to reconstruct a complete representation of a given object relying only on a limited number of vie...

this study is devoted to the formulation of the network design problem of one-way streets and the application of simulated annealing (sa) algorithm to solve this problem for a large real network. it discusses some points of views on one-way street networks, the objective function used for design, the way in which design constraints may be considered, and the traffic problems concerning one-way ...

Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrat...

mutual information (mi) is a widely used similarity metric for multimodality image registration. however, it involves an extremely high computational time especially when it is applied to volume images. moreover, its robustness is affected by existence of local maxima. the multi-resolution pyramid approaches have been proposed to speed up the registration process and increase the accuracy of th...

In today’s economy, manufacturing plants must be able to operate efficiently and respond quickly to changes in product mix and demand. Therefore, this paper considers the problem of arranging and rearranging (when there are changes between the flows of materials between departments) manufacturing facilities such that sum of the material handling and rearrangement costs are minimized. This probl...

In this paper, we propose a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and on Simulated Annealing (SA) for reducing memory energy consumption in embedded systems. Our hybrid algorithm outperforms the Tabu Search (TS) approach. In fact, nearly from 76% up to 98% less energy consumption is recorded.

We will present an algorithm, based on SA-techniques and a sampling procedure, that colors a given random 3-colorable graph with high probability in sublinear time. This result is the rst theoretical justiication of many excellent experimental performance results of Simulated Annealing 10, 17] applied to graph coloring problems.