Linganisha mbinu

	Uboreshaji wa Malengo Mengi ya Kistochastiki	Uboreshaji wa Malengo Mengi Imara
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1990s–2000s	2006
Mwanzilishi	Various (Fonseca, Fleming, Deb, Zitzler, and others)	Deb, K. & Gupta, H.
Aina	Stochastic metaheuristic optimization	Optimization framework
Chanzo asilia	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Deb, K., & Gupta, H. (2006). Introducing robustness in multi-objective optimization. Evolutionary Computation, 14(4), 463–494.
Majina mbadala	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization	RMOO, Robust MOO, Robust Pareto Optimization, Uncertainty-Robust Multi-Objective Optimization
Zinazohusiana	5	4
Muhtasari	Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty.	Robust 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.