this article addresses a single row facility layout problem where the objective is to optimize the arrangement of some rectangular facilities with different dimensions on a line. regarding the np-hard nature of the considered problem, a hybrid meta-heuristic algorithm based on simulated annealing has been proposed to obtain a near optimal solution. a number of test problems are randomly generat...

This presentation demonstrates a simple and very rapid synthesis of metal matrix nanocomposites (MMNCs) in Al-based hybrid systems which are achieved by processing stacked disks of two pure metals through the application of high-pressure torsion (HPT) at ambient temperature. These synthesized hybrid systems exhibit exceptionally high hardness through rapid deformation-induced diffusion and the ...

This article addresses a single row facility layout problem where the objective is to optimize the arrangement of some rectangular facilities with different dimensions on a line. Regarding the NP-Hard nature of the considered problem, a hybrid meta-heuristic algorithm based on simulated annealing has been proposed to obtain a near optimal solution. A number of test problems are randomly generat...

In this paper a hybrid technique of Genetic Algorithm and Simulated Annealing (HGASA) is applied for Fractal Image Compression (FIC). With the help of this hybrid evolutionary algorithm effort is made to reduce the search complexity of matching between range block and domain block. The concept of Simulated Annealing (SA) is incorporated into Genetic Algorithm (GA) in order to avoid pre-mature c...

In this paper 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-heuristics, is ...

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...

Scheduling jobs on computational grids is a compute intensive problem. Existing methods are unable to perform the required breakthrough in terms of time and cost. A Grid scheduler must use the available resources efficiently, while satisfying competing and mutually conflicting goals. The grid workload may consist of multiple jobs, with varying resource requirements and quality-of-service constr...

The present paper focus on the improvement of the efficiency of structural optimization, in typical structural optimization problems there may be many locally minimum configurations. For that reason, the application of a global method, which may escape from the locally minimum points, remain essential. In this paper, a new hybrid simulated annealing algorithm for large scale global optimization...

In short-term production planning, jobs are assigned to machines and scheduled, taking into consideration that operations must be performed in pre-defined sequences. Since the problem is NP-hard, heuristics have to be used. Simulated annealing, neural networks and genetic algorithms are some of the recent approaches. We have tried to improve those methods by taking a hybrid of simulated anneali...