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| Agent-Based NSGA-II× | Multi-Objective Genetic Algorithm (MOGA)× | |
|---|---|---|
| Campo | Simulazione | Simulazione |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 2000s–2010s | 1984 |
| Ideatore≠ | 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) |
| Tipo≠ | Simulation-embedded evolutionary multi-objective optimizer | Population-based evolutionary optimizer |
| Fonte seminale≠ | 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. DOI ↗ | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| Alias | AB-NSGA-II, ABM-NSGA2, agent-driven NSGA-II, simulation-based NSGA-II | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| Correlati | 4 | 4 |
| Sintesi≠ | 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. |
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