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| NSGA-III× | Optimisation multi-objectif× | |
|---|---|---|
| Domaine≠ | Recherche opérationnelle | Simulation |
| Famille≠ | Machine learning | Process / pipeline |
| Année d'origine≠ | 2014 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| Auteur d'origine≠ | Kalyanmoy Deb and Himanshu Jain | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| Type≠ | algorithm | Optimization framework |
| Source fondatrice≠ | Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577-601. DOI ↗ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Alias≠ | NSGA-III algorithm, NSGA-III evolutionary, many-objective optimization | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| Apparentées≠ | 2 | 3 |
| Résumé≠ | NSGA-III (Non-dominated Sorting Genetic Algorithm III), developed by Kalyanmoy Deb and Himanshu Jain in 2014, is a state-of-the-art evolutionary algorithm for many-objective optimization problems. It extends the popular NSGA-II algorithm with reference-point-based selection, enabling effective handling of problems with three or more conflicting objectives. | 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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