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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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Genetic algorithms and simulated annealing for gene mapping
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, 2002Ordering genes on a chromosome is similar to the traveling salesman problem (TSP), but with some extra information. This extra information prompted us to investigate the genetic algorithms (GA) and simulated annealing (SA) methods to solve the gene ordering problem, even though they are not the best methods available to solve the TSP. Comparing the two
John A. Gunnels +2 more
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A MapReduce Enabled Simulated Annealing Genetic Algorithm
2014 International Conference on Identification, Information and Knowledge in the Internet of Things, 2014Intelligent algorithms such as genetic algorithms and simulated annealing algorithms have widely been applied to the field of large scale data analysis and data processing. It is potential for the high-performance distributed computing technologies or platforms to further increase the execution efficiency of these traditional intelligent algorithms ...
Luokai Hu +3 more
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Simulated annealing algorithm in problems of multiprocessor scheduling
Automation and Remote Control, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Daniil A. Zorin, Valery A. Kostenko
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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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Design and Simulation of Simulated Annealing Algorithm with Harmony Search
2010Harmony search is a new heuristic optimization algorithm. Comparing with other algorithms, this algorithm has very strong robustness and can be easily operated. Combining with the features of harmony search, an improved simulated annealing algorithm is proposed in this paper. It can improve the speed of annealing.
Hua Jiang, Yanxiu Liu, Liping Zheng
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Scale Invariance Properties in the Simulated Annealing Algorithm
Methodology And Computing In Applied Probability, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fleischer, M. A., Jacobson, S. H.
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Performance of the simulated annealing algorithm
1987The performance analysis of an approximation algorithm concentrates on the following two quantities: the quality of the final solution obtained by the algorithm, i.e. the difference in cost value between the final solution and a globally minimal configuration; the running time required by the algorithm.
Peter J. M. van Laarhoven +1 more
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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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Genetic Algorithms and Simulated Annealing
2001This chapter introduces the basic concepts and notation of genetic algorithms and simulated annealing, which are two basic search methodologies that can be used for modelling and simulation of complex non-linear dynamical systems. Since both techniques can be considered as general purpose optimization methodologies, we can use them to find the ...
Oscar Castillo, Patricia Melin
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