Methoden vergleichen

	NSGA-III	Mehrzieloptimierung – Gleichzeitige Optimierung widerstreitender Ziele
Fachgebiet	Operations Research	Simulation
Familie	Machine learning	Process / pipeline
Entstehungsjahr	2014	1896 (concept); 1989–2002 (evolutionary algorithms era)
Urheber	Kalyanmoy Deb and Himanshu Jain	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Typ	algorithm	Optimization framework
Wegweisende Quelle	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.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Aliasnamen	NSGA-III algorithm, NSGA-III evolutionary, many-objective optimization	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Verwandt	2	3
Zusammenfassung	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.