Methoden vergleichen

	Policy Scenario Multi-Objective Optimization	Multi-Objective Genetic Algorithm (MOGA)
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1990s–2000s	1984
Urheber	Evolved from multi-objective optimization and policy scenario analysis communities	Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations)
Typ	Scenario-conditioned multi-objective search	Population-based evolutionary optimizer
Wegweisende Quelle	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester. ISBN: 9780471873396	Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673
Aliasnamen	PS-MOO, Policy-Driven MOO, Scenario-Based Multi-Objective Optimization, Policy MOO	MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO
Verwandt	4	4
Zusammenfassung	Policy Scenario Multi-Objective Optimization (PS-MOO) integrates explicit policy scenario construction with multi-objective optimization to identify Pareto-optimal policy options across plausible future states. Decision-makers evaluate trade-offs between competing objectives — such as economic efficiency, equity, and environmental impact — for each distinct policy scenario, then compare Pareto fronts to select robust or scenario-contingent strategies.	A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among.