Topic
Adaptive simulated annealing
About: Adaptive simulated annealing is a research topic. Over the lifetime, 3339 publications have been published within this topic receiving 121743 citations.
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TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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.
41,772 citations
Book•
30 Jun 1987TL;DR: Performance of the simulated annealing algorithm and the relation with statistical physics and asymptotic convergence results are presented.
Abstract: 1 Introduction.- 2 Simulated annealing.- 3 Asymptotic convergence results.- 4 The relation with statistical physics.- 5 Towards implementing the algorithm.- 6 Performance of the simulated annealing algorithm.- 7 Applications.- 8 Some miscellaneous topics.- 9 Summary and conclusions.
3,645 citations
IBM1
TL;DR: Experimental studies of the simulated annealing method are presented and its computational efficiency when applied to graph partitioning and traveling salesman problems are presented.
Abstract: Simulated annealing is a stochastic optimization procedure which is widely applicable and has been found effective in several problems arising in computeraided circuit design. This paper derives the method in the context of traditional optimization heuristics and presents experimental studies of its computational efficiency when applied to graph partitioning and traveling salesman problems.
1,808 citations
TL;DR: This implementation of simulated annealing was used in "Global Optimization of Statistical Functions with Simulated Annealing," Goffe, Ferrier and Rogers, Journal of Econometrics, vol.
1,665 citations
TL;DR: A new global optimization algorithm for functions of continuous variables is presented, derived from the “Simulated Annealing” algorithm recently introduced in combinatorial optimization, which is quite costly in terms of function evaluations, but its cost can be predicted in advance, depending only slightly on the starting point.
Abstract: A new global optimization algorithm for functions of continuous variables is presented, derived from the “Simulated Annealing” algorithm recently introduced in combinatorial optimization.The algorithm is essentially an iterative random search procedure with adaptive moves along the coordinate directions. It permits uphill moves under the control of a probabilistic criterion, thus tending to avoid the first local minima encountered.The algorithm has been tested against the Nelder and Mead simplex method and against a version of Adaptive Random Search. The test functions were Rosenbrock valleys and multiminima functions in 2,4, and 10 dimensions.The new method proved to be more reliable than the others, being always able to find the optimum, or at least a point very close to it. It is quite costly in term of function evaluations, but its cost can be predicted in advance, depending only slightly on the starting point.
1,598 citations