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Multi-Objective Simulated Annealing (MOSA)×Simulated Annealing×
FachgebietSimulationOptimierung
FamilieProcess / pipelineProcess / pipeline
Entstehungsjahr1992–19981983
UrheberSerafini, P.; Czyzak, P. and Jaszkiewicz, A.
TypMetaheuristic / Pareto-based optimizerProbabilistic metaheuristic / local search
Wegweisende QuelleCzyzak, P., Jaszkiewicz, A. (1998). Pareto simulated annealing — a metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis, 7(1), 34–47. DOI ↗Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗
AliasnamenMOSA, Multi-Criteria Simulated Annealing, Pareto Simulated Annealing, PSABenzetimli Tavlama (Simulated Annealing), SA, probabilistic local search
Verwandt55
ZusammenfassungMulti-Objective Simulated Annealing (MOSA) extends the classical simulated annealing metaheuristic to problems with two or more conflicting objective functions. Instead of converging to a single optimum, MOSA explores the solution space stochastically and maintains an archive of non-dominated (Pareto-optimal) solutions, offering decision-makers a diverse trade-off front rather than one prescribed answer.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.
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ScholarGateMethoden vergleichen: Multi-objective simulated annealing · Simulated Annealing. Abgerufen am 2026-06-18 von https://scholargate.app/de/compare