We analyze simulated annealing applied to multiple-valued programmable logic array (MVL PLA) design. Of spec@c interest is the use of parallel processors. We consider the use of loosely-coupled, coarsegrainedparallel systems, and study the relationship between the quality of the solution and computation time, on the one hand, and simulated annealing parameters, start temperature, cooling rate, ...

In today’s competitive transportation systems, passengers search to find traveling agencies that are able to serve them efficiently considering both traveling time and transportation costs. In this paper, we present a new model for the traveling salesman problem with multiple transporters (TSPMT). In the proposed model, which is more applicable than the traditional versions, each city has diffe...

In this communication Simulated Annealing and Genetic Algorithms, are applied to the graph partitioning problem. These techniques mimic processes in statistical mechanics and biology, respectively, and are the most popular meta-heuristics or general-purpose optimization strategies. A hybrid algorithm for circuit partitioning, which uses tabu search to improve the simulated annealing meta-heuris...

The forward kinematics of the 6-6 leg parallel manipulator is solved using hybrid meta-heuristic techniques in which the simulated annealing algorithm replaces the mutation operator in a genetic algorithm. The results are compared with the standard simulated annealing and genetic algorithm. It shows that the standard simulated annealing algorithm outperforms standard genetic algorithm in terms ...

Received on:2/6/2009 Accepted on:1/4/2010 Abstract Simulated annealing (SA) has been considered as a good tool for search and optimization problems which represent the abstraction of obtaining the crystalline structure through a physical process. This algorithm works sequentially that the current state will produce only one next state. That will make the search to be slower and the important dr...

A classes of stochastic algorithms, which are very powerful in the case of the degraded image reconstruction, are simulated annealing based algorithms. However, the reconstruction of a degraded image using iterative stochastic process require a large number of operations and is still out of real time. On the other hand, learning and generalization capability of ANN models allows a large panel o...