Simulated annealing for constrained global optimization
dc.contributor.author | Smith, Robert L. | en_US |
dc.contributor.author | Romeijn, H. Edwin | en_US |
dc.date.accessioned | 2006-09-11T15:27:45Z | |
dc.date.available | 2006-09-11T15:27:45Z | |
dc.date.issued | 1994-09 | en_US |
dc.identifier.citation | Romeijn, H. Edwin; Smith, Robert L.; (1994). "Simulated annealing for constrained global optimization." Journal of Global Optimization 5(2): 101-126. <http://hdl.handle.net/2027.42/44934> | en_US |
dc.identifier.issn | 0925-5001 | en_US |
dc.identifier.issn | 1573-2916 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/44934 | |
dc.description.abstract | Hide-and-Seek is a powerful yet simple and easily implemented continuous simulated annealing algorithm for finding the maximum of a continuous function over an arbitrary closed, bounded and full-dimensional body. The function may be nondifferentiable and the feasible region may be nonconvex or even disconnected. The algorithm begins with any feasible interior point. In each iteration it generates a candidate successor point by generating a uniformly distributed point along a direction chosen at random from the current iteration point. In contrast to the discrete case, a single step of this algorithm may generate any point in the feasible region as a candidate point. The candidate point is then accepted as the next iteration point according to the Metropolis criterion parametrized by an adaptive cooling schedule. Again in contrast to discrete simulated annealing, the sequence of iteration points converges in probability to a global optimum regardless of how rapidly the temperatures converge to zero. Empirical comparisons with other algorithms suggest competitive performance by Hide-and-Seek. | en_US |
dc.format.extent | 1150830 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Kluwer Academic Publishers; Springer Science+Business Media | en_US |
dc.subject.other | Optimization | en_US |
dc.subject.other | Continuous Simulated Annealing | en_US |
dc.subject.other | Monte Carlo Optimization | en_US |
dc.subject.other | Computer Science, General | en_US |
dc.subject.other | Economics / Management Science | en_US |
dc.subject.other | Real Functions | en_US |
dc.subject.other | Operation Research/Decision Theory | en_US |
dc.subject.other | Adaptive Cooling | en_US |
dc.subject.other | Random Search | en_US |
dc.subject.other | Global Optimization | en_US |
dc.title | Simulated annealing for constrained global optimization | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Industrial and Operations Engineering, The University of Michigan, 48109-2117, Ann Arbor, Michigan, USA | en_US |
dc.contributor.affiliationother | Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, The Netherlands | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/44934/1/10898_2005_Article_BF01100688.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/BF01100688 | en_US |
dc.identifier.source | Journal of Global Optimization | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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