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| 다목적 시뮬레이티드 어닐링 (MOSA)× | 다목적 최적화× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1992–1998 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| 창시자≠ | Serafini, P.; Czyzak, P. and Jaszkiewicz, A. | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| 유형≠ | Metaheuristic / Pareto-based optimizer | Optimization framework |
| 원전≠ | 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 ↗ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| 별칭 | MOSA, Multi-Criteria Simulated Annealing, Pareto Simulated Annealing, PSA | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| 관련≠ | 5 | 3 |
| 요약≠ | 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. | 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. |
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