Methoden vergleichen

	Multi-Objective Simulated Annealing (MOSA)	Mehrzieloptimierung – Gleichzeitige Optimierung widerstreitender Ziele
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1992–1998	1896 (concept); 1989–2002 (evolutionary algorithms era)
Urheber	Serafini, P.; Czyzak, P. and Jaszkiewicz, A.	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Typ	Metaheuristic / Pareto-based optimizer	Optimization framework
Wegweisende Quelle	Czyzak, 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.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Aliasnamen	MOSA, Multi-Criteria Simulated Annealing, Pareto Simulated Annealing, PSA	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Verwandt	5	3
Zusammenfassung	Multi-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.	Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis.