Showing papers on "Simulated annealing published in 1970"

How To Schedule A Flow Shop Plant By Agents

Abstract: The flow shop scheduling problem consists, according to a certain number of criteria, in finding the best possible allocation of n jobs on m resources, so that operations of every job must be processed on all resources in a unique order. Because of its highly combinatorial aspect, this scheduling procedure has been widely studied in the literature by exact and mostly heuristic methods. The approach, we adopt here to deal with this problem, combines a Multi-Agent system with a stochastic combinatorial optimization tool, the simulated annealing. This paper stresses the efficiency and the optimality of a distributed implementation of this tool compared to a classical one.

Left Ventricle 3D Reconstruction Using GibbsRandom Fields And Simulated Annealing

Abstract: This paper proposes a left ventricle tridimensional reconstruction method from two orthogonal X-rays angiographic projections. The algorithm works under the assumption of having two segmented parallel projections and an homogeneous mixture of blood and contrast agent, in order to develop a binary reconstruction based on a Markov Random Field model and Simulated Annealing. The 3D ventricular object is considered as a stacked bidimensional slice set and each slice is reconstructed from the two one-dimensional profiles corresponding to a pair of rows obtained from the segmented projections. Each bidimensional slice is described in a polar coordinate reference system as a function n = R(& ,•) that describes each point in the slice contour. This discrete onedimensional function describing the two-dimensional slice is modeled as a non-causal Markov Random Field, where the conditional probability of one point given the rest of points is equivalent to the conditional probability of the same point given the points belonging to a neighborhood. The slice joint probability distribution is deduced by considering the equivalence between the Gibbs and Markov Random Fields. This joint probability is defined by an energy function including the local potential interaction between the sites included in a neighborhood. The energy function depends on the projections errors of the reconstructed slice, its connexity and the 3D spatial regularity. The proposed algorithm starts with a provided initial approximate reconstruction that is then appropriately deformed to obtain the most probable slice form. Such deformation process is performed by using the probabilistic Gibbs model and the Simulated Annealing in order to minimize the energy function. Performance of the reconstruction method was evaluated with preprocessed ventricular angiographic images and a 3D binary database. The results are promising as the reconstruction error is less than 7%. Transactions on Biomedicine and Health vol 3, © 1996 WIT Press, www.witpress.com, ISSN 1743-3525 240 Simulation Modelling in Bioengineering

Partitioning unstructured meshes using a distributed optimization model

Abstract: Large meshes computations arise in many large-scale scientific and engineering problems, including finite volume methods for computational fluid dynamics, and finite element methods for structure analysis. If these meshes have to be solved efficiently on distributed memory parallel processors, a partitioning strategy should be designed so that on the one hand, processors have approximately equal work to do, and on the other hand inter-processor communication is minimized. In this paper we introduce a distributed optimization model combining Multi-agent systems and simulated annealing for the mesh partitioning problem.

Parallel Implementation Of SimulatedAnnealing By Distributed Memory Systems

Abstract: The aim of this paper is to provide a parallel strategy to be used when implementing the Simulated Annealing process by distributed memory systems. It focuses the attention on the portfolio selection problem with dynamic and integer constraints on the variables. However, this method can be extended to any dynamic optimisation problem as the optimal control. In the first part it develops the sequential algorithm of Simulated Annealing stressing some features inherent in the application itself: the structure of the neighbourhood of the solution, the starting value and the decreasing schedule of the control parameter. In the second part it deals with the formulation of a parallel strategy for the algorithm. It starts from the consideration that an optimisation technique with a probabilistic searching criterion cannot give a reliable result in a single execution. Therefore it proposes a strategy performing a number of independent concurrent Simulated Annealing processes applied to different initial configurations of the same problem. Finally, it proposes a co-operation policy among processes, by means of a reduced number of communications. This policy enhance the efficiency of the parallel computing system.

Optimum Design With Multimodal SimulatedAnnealing Strategies

Abstract: Strategies for locating multiple relative optima in a multimodal optimization problem using multiple-point simulated annealing are proposed. Two strategies incorporated with multiple-point simulated annealing are developed to increase the capabilities of locating more relative optima in a nonconvex design space. Balance strategy involves the use of a balance function that evaluates the degree of design spreading over the entire design space and create a corresponding direction bias in the subsequent design-change process. Bounce strategy evaluates the degree of design crowding and aims to push designs away from the crowd center by creating a direction bias in the next design-change process. Both strategies are evaluated on a number of illustrative multimodal problems.

Modified Iterated Simulated Annealing ForOptimal Structural Design

Abstract: A modified iterated simulated annealing (MISA) method is presented for optimal design of structural systems. The method uses a random sequence of designs to determine the optimal one. Automatic reduction of the feasible region and sensitivity analysis for the design variables are also used. Comparisons between classical optimization methods and MISA show that the letter can provide accurate results even when infeasible initial designs are attempted, and is advantageous for problems with dynamic constraints.