विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| Bayesian NSGA-II× | बहु-उद्देश्यीय अनुकूलन× | |
|---|---|---|
| क्षेत्र | अनुकरण | अनुकरण |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 2002–2006 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| प्रवर्तक≠ | Emmerich, M. T. M. et al. (surrogate-assisted MO-EA); Deb et al. (NSGA-II base) | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| प्रकार≠ | Surrogate-assisted multi-objective evolutionary algorithm | Optimization framework |
| मौलिक स्रोत≠ | 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 |
| उपनाम | B-NSGA-II, Surrogate-Assisted NSGA-II, Gaussian Process NSGA-II, Bayesian Multi-Objective EA | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| संबंधित | 3 | 3 |
| सारांश≠ | Bayesian NSGA-II integrates Gaussian process surrogate models (Bayesian metamodels) into the NSGA-II evolutionary loop to solve expensive multi-objective optimization problems. By replacing costly true function evaluations with fast probabilistic predictions, it discovers high-quality Pareto-front approximations with far fewer real evaluations than standard NSGA-II. | 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. |
| ScholarGateडेटासेट ↗ |
|
|