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确定性多目标优化 — 经典帕累托方法与标量化方法
确定性多目标优化（Deterministic Multi-Objective Optimization, 确定性 MOO）是一类经典的优化方法，旨在在确定的可行集上同时最小化或最大化多个相互冲突的目标函数。它会生成一个帕累托前沿（Pareto front）—— 即非支配解集 —— 决策者从中选择偏好的权衡方案。与随机变体不同，所有目标评估和约束都是固定且无噪声的。
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来源
- 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
如何引用本页
ScholarGate. (2026, June 3). Deterministic Multi-Objective Optimization — Classical Pareto-based and scalarization approaches without stochastic components. ScholarGate. https://scholargate.app/zh/simulation/deterministic-multi-objective-optimization
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