方法证据记录
Deterministic Multi-Objective Optimization
Deterministic Multi-Objective Optimization (Deterministic MOO) is a family of classical optimization approaches that simultaneously minimize or maximize multiple conflicting objective functions over a deterministic feasible set. It produces a Pareto front — the set of non-dominated solutions — from which a decision-maker selects the preferred trade-off. Unlike stochastic variants, all objective evaluations and constraints are fixed and noise-free.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Deterministic Multi-Objective Optimization — Classical Pareto-based and scalarization approaches without stochastic components
分类方法记录 · process-pipeline / simulation
- Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. · ISBN 978-0-471-87339-6
- Miettinen, K. (1999). Nonlinear Multiobjective Optimization. Springer, Boston. · ISBN 978-1-4613-7544-9
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