A Combined Multistart-Annealing Algorithm for Continuous Global Optimization.

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dc.contributor.authorPiccioni, M.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T09:37:00Z
dc.date.available2007-05-23T09:37:00Z
dc.date.issued1987en_US
dc.description.abstractThe application of simulated annealing to the global optimization of a function on a compact subset of R^d is discussed. For the Langevin algorithm the class of convergent schedules depends on some a priori knowledge about the form of the function. It is shown that this problem disappears by using the simplest annealing algorithm of jump type, which can also be improved by performing local searches between two consecutive jump times. The resulting algorithm is essentially a multistart technique controlled by an annealing schedule.en_US
dc.format.extent516979 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/4554
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1987-45en_US
dc.titleA Combined Multistart-Annealing Algorithm for Continuous Global Optimization.en_US
dc.typeTechnical Reporten_US

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