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| Waloptimierungsalgorithmus (WOA)× | Partikelschwarmoptimierung (PSO)× | |
|---|---|---|
| Fachgebiet | Optimierung | Optimierung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2016 | 1995 |
| Urheber≠ | Seyedali Mirjalili & Andrew Lewis | — |
| Typ≠ | Swarm-based metaheuristic | Population-based metaheuristic / swarm intelligence |
| Wegweisende Quelle≠ | Mirjalili, S. & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67. DOI ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| Aliasnamen | WOA, Balina Optimizasyon Algoritması (WOA), bubble-net attacking method | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | The Whale Optimization Algorithm (WOA) is a swarm-based metaheuristic introduced by Mirjalili and Lewis in 2016. It models the bubble-net hunting strategy of humpback whales, in which a group of whales spirals around prey while gradually tightening the encirclement. The algorithm balances global exploration and local exploitation through a small set of parameters and has become widely used in continuous engineering optimisation problems. | 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. |
| ScholarGateDatensatz ↗ |
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