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| Многоцелева оптимизация× | Оптимизация чрез рояк от частици (PSO)× | |
|---|---|---|
| Област≠ | Симулационно моделиране | Оптимизация |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1896 (concept); 1989–2002 (evolutionary algorithms era) | 1995 |
| Създател≠ | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. | — |
| Тип≠ | Optimization framework | Population-based metaheuristic / swarm intelligence |
| Основополагащ източник≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| Други названия≠ | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Свързани≠ | 3 | 6 |
| Резюме≠ | 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. | Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems. |
| ScholarGateНабор от данни ↗ |
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