Compară metode

	Agent-Based NSGA-II	Stochastic NSGA-II
Domeniu	Simulare	Simulare
Familie	Process / pipeline	Process / pipeline
Anul apariției	2000s–2010s	2001–2002
Autorul original	Deb et al. (NSGA-II, 2002); integrated with agent-based modeling frameworks in the 2000s–2010s	Deb, K. et al. (NSGA-II base); Hughes, E. J. and subsequent researchers for stochastic extensions
Tip	Simulation-embedded evolutionary multi-objective optimizer	Evolutionary multi-objective optimization under uncertainty
Sursa seminală	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.	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.
Denumiri alternative	AB-NSGA-II, ABM-NSGA2, agent-driven NSGA-II, simulation-based NSGA-II	S-NSGA-II, NSGA-II under Uncertainty, Stochastic Multi-Objective NSGA-II, Robust NSGA-II
Înrudite	4	5
Rezumat	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.	Stochastic NSGA-II extends the NSGA-II evolutionary algorithm to handle objective functions that are noisy, uncertain, or probabilistic. By averaging or sampling stochastic objectives across multiple evaluations, it identifies Pareto-optimal solutions that are robust to uncertainty, making it suitable for engineering design, supply chain, and policy optimization problems where real-world variability matters.