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| 다목적 유전 알고리즘 (MOGA)× | 다목적 시뮬레이티드 어닐링 (MOSA)× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1984 | 1992–1998 |
| 창시자≠ | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) | Serafini, P.; Czyzak, P. and Jaszkiewicz, A. |
| 유형≠ | Population-based evolutionary optimizer | Metaheuristic / Pareto-based optimizer |
| 원전≠ | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 | 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 ↗ |
| 별칭 | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO | MOSA, Multi-Criteria Simulated Annealing, Pareto Simulated Annealing, PSA |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. | 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. |
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