Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Algorithme des lucioles× | Algorithme génétique× | |
|---|---|---|
| Domaine | Optimisation | Optimisation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2008 | 1975 |
| Auteur d'origine≠ | Xin-She Yang | John Henry Holland |
| Type≠ | Swarm intelligence metaheuristic | Population-based metaheuristic |
| Source fondatrice≠ | Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84. DOI ↗ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| Alias | FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm) | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail. |
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