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Робастна многокритериална оптимизация×Анализ на чувствителността×
ОбластСимулационно моделиранеВземане на решения
СемействоProcess / pipelineMCDM
Година на възникване20062004
СъздателDeb, K. & Gupta, H.Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
ТипOptimization frameworkRobustness wrapper — parameter / weight perturbation sensitivity indices
Основополагащ източникDeb, K., & Gupta, H. (2006). Introducing robustness in multi-objective optimization. Evolutionary Computation, 14(4), 463–494. DOI ↗Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester DOI ↗
Други названияRMOO, Robust MOO, Robust Pareto Optimization, Uncertainty-Robust Multi-Objective Optimization
Свързани40
РезюмеRobust Multi-Objective Optimization (RMOO) is a framework for finding solutions that simultaneously optimize multiple conflicting objectives while remaining insensitive to perturbations in decision variables or problem parameters. Unlike classical MOO, RMOO explicitly incorporates uncertainty into the optimization loop, producing a robust Pareto front whose members perform well not only at the nominal design point but also across a neighbourhood of plausible operating conditions.SENSITIVITY-ANALYSIS (Sensitivity Analysis — Systematic assessment of output variation w.r.t. input perturbations) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
ScholarGateНабор от данни
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  2. 2 Източници
  3. PUBLISHED
  1. v1
  2. 1 Източници
  3. PUBLISHED

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ScholarGateСравнение на методи: Robust Multi-Objective Optimization · SENSITIVITY-ANALYSIS. Извлечено на 2026-06-15 от https://scholargate.app/bg/compare