Methoden vergleichen

	Multi-Objective Simulated Annealing (MOSA)	Multi-Objective Particle Swarm Optimization (MOPSO)
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1992–1998	2004
Urheber	Serafini, P.; Czyzak, P. and Jaszkiewicz, A.	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
Typ	Metaheuristic / Pareto-based optimizer	Population-based swarm metaheuristic
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.	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279.
Aliasnamen	MOSA, Multi-Criteria Simulated Annealing, Pareto Simulated Annealing, PSA	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
Verwandt	5	5
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 Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of candidate solutions toward the true Pareto front without requiring a priori preference information.