পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| Bayesian NSGA-II× | বহু-উদ্দেশ্যমূলক অপ্টিমাইজেশান× | |
|---|---|---|
| ক্ষেত্র | অনুকরণ | অনুকরণ |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 2002–2006 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| প্রবর্তক≠ | 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. |
| ধরন≠ | Surrogate-assisted multi-objective evolutionary algorithm | Optimization framework |
| মৌলিক উৎস≠ | 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 |
| অপর নাম | B-NSGA-II, Surrogate-Assisted NSGA-II, Gaussian Process NSGA-II, Bayesian Multi-Objective EA | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| সম্পর্কিত | 3 | 3 |
| সারসংক্ষেপ≠ | 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. |
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