Linganisha mbinu

	Uboreshaji wa Malengo Mengi ya Kistochastiki	Utekelezaji Sanifu wa Kielelezo
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1990s–2000s	1957
Mwanzilishi	Various (Fonseca, Fleming, Deb, Zitzler, and others)	Bellman, R.; formalized for stochastic settings by Puterman, M. L.
Aina	Stochastic metaheuristic optimization	Sequential optimization under uncertainty
Chanzo asilia	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
Majina mbadala	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization	SDP, Markov Decision Process, MDP, Stochastic DP
Zinazohusiana	5	6
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.	Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods.