विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| रैखिक प्रोग्रामन× | बहु-उद्देश्यीय अनुकूलन× | |
|---|---|---|
| क्षेत्र≠ | अनुकूलन | अनुकरण |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1947 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| प्रवर्तक≠ | George B. Dantzig | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| प्रकार≠ | Mathematical programming / continuous optimization | Optimization framework |
| मौलिक स्रोत≠ | Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press. ISBN: 9780691059136 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| उपनाम≠ | LP, linear optimization, Doğrusal Programlama (LP) | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| संबंधित≠ | 4 | 3 |
| सारांश≠ | Linear programming (LP), pioneered by George B. Dantzig in 1947, is a mathematical method for finding the best value of a linear objective function — such as minimum cost or maximum profit — subject to a set of linear inequality and equality constraints. It is the foundational technique in operations research and underlies production planning, resource allocation, logistics, diet problems, and countless other decision-making scenarios across engineering, economics, and the natural sciences. | 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. |
| ScholarGateडेटासेट ↗ |
|
|