Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Optimizare Multi-Obiectiv× | Algoritm Genetic× | |
|---|---|---|
| Domeniu≠ | Simulare | Optimizare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1896 (concept); 1989–2002 (evolutionary algorithms era) | 1975 |
| Autorul original≠ | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. | John Henry Holland |
| Tip≠ | Optimization framework | Population-based metaheuristic |
| Sursa seminală≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| Denumiri alternative≠ | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| Înrudite≠ | 3 | 5 |
| Rezumat≠ | 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. | A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail. |
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