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| Multi-Objective Simulated Annealing (MOSA)× | Multi-Objective Genetic Algorithm (MOGA)× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1992–1998 | 1984 |
| Urheber≠ | Serafini, P.; Czyzak, P. and Jaszkiewicz, A. | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| Typ≠ | Metaheuristic / Pareto-based optimizer | Population-based evolutionary optimizer |
| Wegweisende Quelle≠ | Czyzak, P., Jaszkiewicz, A. (1998). Pareto simulated annealing — a metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis, 7(1), 34–47. DOI ↗ | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| Aliasnamen | MOSA, Multi-Criteria Simulated Annealing, Pareto Simulated Annealing, PSA | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | Multi-Objective Simulated Annealing (MOSA) extends the classical simulated annealing metaheuristic to problems with two or more conflicting objective functions. Instead of converging to a single optimum, MOSA explores the solution space stochastically and maintains an archive of non-dominated (Pareto-optimal) solutions, offering decision-makers a diverse trade-off front rather than one prescribed answer. | 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. |
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