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| NSGA-II Mạnh Mẽ× | Tối ưu hóa đa mục tiêu× | |
|---|---|---|
| Lĩnh vực | Mô phỏng | Mô phỏng |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 2006 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| Người khởi xướng≠ | Kalyanmoy Deb and Himanshu Gupta | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| Loại≠ | Robust evolutionary multi-objective optimization algorithm | Optimization framework |
| Công trình gốc≠ | 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 ↗ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Tên gọi khác | Robust NSGA2, NSGA-II under uncertainty, Uncertainty-aware NSGA-II, RNSGA-II | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| Liên quan≠ | 5 | 3 |
| Tóm tắt≠ | Robust NSGA-II extends the classic NSGA-II evolutionary algorithm to account for parametric uncertainty, finding Pareto-optimal trade-off solutions that remain high-performing even when input parameters deviate from their nominal values. Instead of optimizing objective values at a single point, it evaluates each candidate solution across a range or distribution of uncertainty realizations and selects for robustness alongside Pareto dominance. | 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. |
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