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	Multi-Objective Simulated Annealing (MOSA)	Multi-objective Tabu Search (MOTS)
Ämnesområde	Simulering	Simulering
Familj	Process / pipeline	Process / pipeline
Ursprungsår	1992–1998	1997
Upphovsperson	Serafini, P.; Czyzak, P. and Jaszkiewicz, A.	Hansen, M. P.; building on Glover (1989) Tabu Search
Typ	Metaheuristic / Pareto-based optimizer	Metaheuristic multi-objective optimization
Ursprungskälla	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.	Hansen, M. P. (1997). Tabu search for multiobjective optimization: MOTS. Presented at the 13th International Conference on Multiple Criteria Decision Making (MCDM), Cape Town, South Africa.
Alias	MOSA, Multi-Criteria Simulated Annealing, Pareto Simulated Annealing, PSA	MOTS, Multi-criteria Tabu Search, Pareto Tabu Search, TSMOO
Närliggande	5	5
Sammanfattning	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 Tabu Search (MOTS) is a metaheuristic algorithm that extends the classic Tabu Search framework to simultaneously optimize two or more conflicting objective functions. Instead of a single optimum, it seeks to approximate the Pareto front — the set of solutions where no objective can be improved without worsening another — making it suitable for complex combinatorial and continuous optimization problems in engineering, logistics, and operations research.