Showing papers on "Simulated annealing published in 1983"

Optimization by Simulated Annealing

Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

Global Wiring by Simulated Annealing

Abstract: Simulated annealing, a new general-purpose method of multivariate optimization, is applied to global wire routing for both idealized (synthetic) and actual designs of realistic size and complexity. Since the simulated annealing results are better than those obtained by conventional methods we use them as a standard against which to compare several sequential or greedy strategies commonly employed in automatic wiring programs.

Optimization by simulated annealing: A preliminary computational study for the TSP

Abstract: In recent papers by Kirkpatrick et al(1982,1983), an analogy between the statistical mechanics of large multivariate physical systems and combinatorial optimization is presented and used to develop a general strategy for solving discrete optimization problems The method relies on probabilistically accepting intermediate increases in the objective function through a set of user-controlled parameters It is argued that by taking such controlled uphill steps, from time to time, a high quality solution can be found in a moderate amount of computer time This paper applies an implementation of the proposed algorithm to the TSP for various size networks The results show the algorithm to be inferior to several well-known heuristics in terms of both solution quality and computer time expended In addition, set-up time for parameter selection constitutes a major burden for the user Sensitivity of the algorithm to changes in stopping rules and parameter selection is demonstrated through extensive computational experiments

Reconstruction of objects from coded images by simulated annealing

Abstract: A new Monte Carlo reconstruction procedure is presented for retrieval of objects from their coded images. The reconstruction process is modeled as an optimization problem whose cost function is related to how well the coded-image constraints are satisfied. Reduction of the cost function is achieved by an annealing process analogous to the cooling of a melt to produce an ordered crystal. The method is demonstrated by reconstructing two two-dimensional objects from their one-dimensional coded images.