Σύγκριση μεθόδων

	Πολυ-στόχος Βελτιστοποίηση Σωματιδίων (MOPSO)	Προσομοιωμένη Ανόπτηση Πολλαπλών Στόχων (MOSA)
Πεδίο	Προσομοίωση	Προσομοίωση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2004	1992–1998
Δημιουργός	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.	Serafini, P.; Czyzak, P. and Jaszkiewicz, A.
Τύπος	Population-based swarm metaheuristic	Metaheuristic / Pareto-based optimizer
Θεμελιώδης πηγή	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.	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.
Εναλλακτικές ονομασίες	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO	MOSA, Multi-Criteria Simulated Annealing, Pareto Simulated Annealing, PSA
Συναφείς	5	5
Σύνοψη	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.	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.