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| Fledermaus-Algorithmus× | Partikelschwarmoptimierung (PSO)× | |
|---|---|---|
| Fachgebiet | Optimierung | Optimierung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2010 | 1995 |
| Urheber≠ | Xin-She Yang | — |
| Typ≠ | Population-based swarm intelligence | Population-based metaheuristic / swarm intelligence |
| Wegweisende Quelle≠ | Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO), 65–74. DOI ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| Aliasnamen≠ | BA, Bat-Inspired Algorithm, Echolocation-Based Optimization, Yarasa Algoritması | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Verwandt≠ | 3 | 6 |
| Zusammenfassung≠ | The Bat Algorithm (BA) is a nature-inspired metaheuristic optimization method proposed by Xin-She Yang in 2010. It mimics the echolocation behavior of microbats to balance global exploration and local exploitation. Each artificial bat adjusts its position, velocity, and emission frequency, with loudness and pulse rate dynamically controlling the transition from broad search to refined local tuning. BA is suited to continuous and combinatorial optimization problems across engineering, scheduling, and machine learning domains. | 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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