Σύγκριση μεθόδων

	Μοντελοποίηση Βασισμένη σε Πράκτορες Πολλαπλών Στόχων	Βελτιστοποίηση Πολλαπλών Στόχων
Πεδίο	Προσομοίωση	Προσομοίωση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2001-2006	1896 (concept); 1989–2002 (evolutionary algorithms era)
Δημιουργός	Deb, K.; Tesfatsion, L. et al.	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Τύπος	Simulation-optimization hybrid	Optimization framework
Θεμελιώδης πηγή	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester. ISBN: 9780471873396	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Εναλλακτικές ονομασίες	MO-ABM, Multi-objective ABM, Pareto-based agent-based modeling, Multi-objective agent simulation	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Συναφείς	4	3
Σύνοψη	Multi-Objective Agent-Based Modeling (MO-ABM) couples agent-based simulation with multi-objective optimization to simultaneously optimize several conflicting performance criteria across complex adaptive systems. Autonomous agents interact according to behavioral rules while an optimizer searches for parameter configurations that achieve Pareto-optimal trade-offs among competing system-level goals.	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.