قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| البرمجة الهدفية العشوائية× | التحسين العشوائي متعدد الأهداف× | |
|---|---|---|
| المجال | المحاكاة | المحاكاة |
| العائلة | Process / pipeline | Process / pipeline |
| سنة النشأة≠ | 1968 | 1990s–2000s |
| صاحب الطريقة≠ | Contini, B. (building on Charnes & Cooper's chance-constrained programming) | Various (Fonseca, Fleming, Deb, Zitzler, and others) |
| النوع≠ | Stochastic multi-goal optimization | Stochastic metaheuristic optimization |
| المصدر التأسيسي≠ | Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| الأسماء البديلة | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization |
| ذات صلة≠ | 6 | 5 |
| الملخص≠ | Stochastic Goal Programming (SGP) extends classical goal programming to handle uncertainty in goal targets, constraint coefficients, or right-hand-side parameters. By incorporating probabilistic constraints and stochastic objective components, it finds solutions that satisfy multiple goals at acceptable probability levels, making it suitable for decision problems where data are inherently uncertain or variable. | 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. |
| ScholarGateمجموعة البيانات ↗ |
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