قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| التحسين متعدد الأهداف× | البرمجة الصحيحة المختلطة× | |
|---|---|---|
| المجال | المحاكاة | المحاكاة |
| العائلة | Process / pipeline | Process / pipeline |
| سنة النشأة≠ | 1896 (concept); 1989–2002 (evolutionary algorithms era) | 1958–1960 |
| صاحب الطريقة≠ | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| النوع≠ | Optimization framework | Mathematical optimization |
| المصدر التأسيسي≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 |
| الأسماء البديلة | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| ذات صلة≠ | 3 | 6 |
| الملخص≠ | Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis. | Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally. |
| ScholarGateمجموعة البيانات ↗ |
|
|