Methoden vergleichen

	Mehrzieloptimierung – Gleichzeitige Optimierung widerstreitender Ziele	Genetischer Algorithmus	Mixed-Integer Programming
Fachgebiet	Simulation	Optimierung	Simulation
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Entstehungsjahr	1896 (concept); 1989–2002 (evolutionary algorithms era)	1975	1958–1960
Urheber	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.	John Henry Holland	Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
Typ	Optimization framework	Population-based metaheuristic	Mathematical optimization
Wegweisende Quelle	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
Aliasnamen	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming
Verwandt	3	5	6
Zusammenfassung	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.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.	Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.