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DomaineSimulationOptimisation
FamilleProcess / pipelineProcess / pipeline
Année d'origine19901983
Auteur d'origineRose, K., Gurewitz, E., Fox, G. C.
TypeDeterministic metaheuristic — annealing schedule without probabilistic acceptanceProbabilistic metaheuristic / local search
Source fondatriceRose, K., Gurewitz, E., Fox, G. C. (1990). A deterministic annealing approach to clustering. Pattern Recognition Letters, 11(9), 589-594. DOI ↗Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗
AliasDSA, Deterministic Annealing, Greedy Annealing, Temperature-Scheduled DescentBenzetimli Tavlama (Simulated Annealing), SA, probabilistic local search
Apparentées25
RésuméDeterministic Simulated Annealing (DSA) is an optimization metaheuristic that adopts the cooling-schedule structure of classical simulated annealing but replaces the probabilistic Metropolis acceptance criterion with a strictly deterministic rule: only improving moves are accepted. This yields a reproducible, greedy-descent procedure guided by an annealing temperature schedule.Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems.
ScholarGateJeu de données
  1. v1
  2. 2 Sources
  3. PUBLISHED
  1. v1
  2. 2 Sources
  3. PUBLISHED

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ScholarGateComparer des méthodes: Deterministic Simulated Annealing · Simulated Annealing. Consulté le 2026-06-18 sur https://scholargate.app/fr/compare