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| Wielokryterialna optymalizacja rojem cząstek (MOPSO)× | Optymalizacja rojem cząstek (PSO)× | |
|---|---|---|
| Dziedzina≠ | Symulacja | Optymalizacja |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2004 | 1995 |
| Twórca≠ | Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. | — |
| Typ≠ | Population-based swarm metaheuristic | Population-based metaheuristic / swarm intelligence |
| Źródło pierwotne≠ | Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279. DOI ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| Inne nazwy≠ | MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Pokrewne≠ | 5 | 6 |
| Podsumowanie≠ | Multi-Objective Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of candidate solutions toward the true Pareto front without requiring a priori preference information. | 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. |
| ScholarGateZbiór danych ↗ |
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