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The Simulated Annealing Algorithm
1988Most placement and global routing problems belong to the class of NP-complete problems.1 For problems in this class, there is no known exact algorithm whose worst-case time complexity is bounded by a polynomial in the size of the input. Consequently, heuristic algorithms are used to find solutions to these problems.
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A Fast Algorithm for Simulated Annealing
Physica Scripta, 1991We present a new deterministic algorithm for simulated annealing and demonstrate its applicability with several classical examples: the ground state energies of the 2d and 3d short range Ising spin glasses, the traveling salesman problem, and pattern recognition in computer vision.
Hong Guo +3 more
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Implementing a Parallel Simulated Annealing Algorithm
2010The MPI and OpenMP implementations of the parallel simulated annealing algorithm solving the vehicle routing problem (VRPTW) are presented. The algorithm consists of a number of components which co-operate periodically by exchanging their best solutions found to date.
Zbigniew J. Czech +2 more
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Parallel algorithms for chip placement by simulated annealing
IBM Journal of Research and Development, 1987We explore modifications to the standard simulated annealing method for circuit placement which make it more suitable for use on a shared-memory parallel computer. By employing chaotic approaches we allow the parallel algorithms to deviate from the algorithm defined for a serial computer and thus obtain good execution efficiencies for large numbers of ...
Frederica Darema +2 more
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On the convergence of stationary distributions in simulated annealing algorithms
Information Processing Letters, 1988Simulated annealing is an iterative probabilistic algorithm designed to find good solutions for discrete optimization problems. At each iterative step a deterioration of the objective function is accepted with a certain probability. This probability depends upon a parameter t called the temperature and approaches zero as t approaches zero. Thus optimal
Ulrich Faigle, Rainer Schrader
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A segmented algorithm for simulated annealing
Statistics and Computing, 1992The properties of a parameterized form of generalized simulated annealing for function minimization are investigated by studying the properties of repeated minimizations from random starting points. This leads to the comparison of distributions of function values and of numbers of function evaluations.
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Simulated annealing algorithm for virtual network reconfiguration
Proceedings of the 8th Euro-NF Conference on Next Generation Internet NGI 2012, 2012Network virtualization has evoked interest among researchers as a promising solution for developing flexible and manageable architectures for future Internet. One of the major challenges in network virtualization is the efficient allocation of substrate resources to the Virtual Networks (VNs) - a problem known as Virtual Network Embedding (VNE).
Sarang Bharadwaj Masti +1 more
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Simulated annealing algorithms: an overview
IEEE Circuits and Devices Magazine, 1989A brief introduction is given to the actual mechanics of simulated annealing, and a simple example from an IC layout is used to illustrate how these ideas can be applied. The complexities and tradeoffs involved in attacking a realistically complex design problem are illustrated by dissecting two very different annealing algorithms for VLSI chip ...
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A Simulated Annealing Algorithm for GPU Clusters
2012Simulated Annealing (SA) is a powerful global optimization technique that is frequently used for solving many practical problems from various scientific and technical fields. In this article we present a novel approach to parallelization of SA and propose an algorithm optimized for execution in GPU clusters.
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A nested simulated annealing algorithm
Journal of Statistical Computation and Simulation, 1995The paper describes a new nested simulated annealing algorithm which is a modification of the usual simulated annealing algorithm. In the nested algorithm super-cooling phases are introduced into the normal annealing process, to speed up the evolution of good solutions.
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