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| Fledermaus-Algorithmus× | Firefly Algorithm× | |
|---|---|---|
| Fachgebiet | Optimierung | Optimierung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2010 | 2008 |
| Urheber | Xin-She Yang | Xin-She Yang |
| Typ≠ | Population-based swarm intelligence | Swarm intelligence metaheuristic |
| Wegweisende Quelle≠ | Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO), 65–74. DOI ↗ | Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84. DOI ↗ |
| Aliasnamen≠ | BA, Bat-Inspired Algorithm, Echolocation-Based Optimization, Yarasa Algoritması | FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm) |
| Verwandt≠ | 3 | 5 |
| 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. | The Firefly Algorithm (FA), introduced by Xin-She Yang in 2008 and formally published in 2010, is a nature-inspired swarm metaheuristic that models the bioluminescent attraction behaviour of fireflies. Each candidate solution is a firefly whose brightness represents its objective-function value; dimmer fireflies move toward brighter ones with an attraction force that decays with distance, driving the swarm toward optima without gradient information. |
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