Σύγκριση μεθόδων

	Agent-Based NSGA-II	Γενετικός Αλγόριθμος Πολλαπλών Στόχων (MOGA)
Πεδίο	Προσομοίωση	Προσομοίωση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2000s–2010s	1984
Δημιουργός	Deb et al. (NSGA-II, 2002); integrated with agent-based modeling frameworks in the 2000s–2010s	Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations)
Τύπος	Simulation-embedded evolutionary multi-objective optimizer	Population-based evolutionary optimizer
Θεμελιώδης πηγή	Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.	Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673
Εναλλακτικές ονομασίες	AB-NSGA-II, ABM-NSGA2, agent-driven NSGA-II, simulation-based NSGA-II	MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO
Συναφείς	4	4
Σύνοψη	Agent-based NSGA-II embeds the NSGA-II evolutionary algorithm inside an agent-based simulation loop so that objective values for each candidate solution are determined by running a full agent simulation rather than by evaluating a closed-form function. This coupling enables multi-objective optimization over systems whose performance emerges from the micro-level interactions of autonomous agents rather than from analytically tractable equations.	A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among.