Salīdzināt metodes
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| Firefly Algorithm× | Diferenciālā evolūcija× | |
|---|---|---|
| Nozare | Optimizācija | Optimizācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2008 | 1997 |
| Autors≠ | Xin-She Yang | Rainer Storn & Kenneth Price |
| Tips≠ | Swarm intelligence metaheuristic | Population-based stochastic metaheuristic |
| Pirmavots≠ | Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84. DOI ↗ | Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359. DOI ↗ |
| Citi nosaukumi | FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm) | DE algorithm, Diferansiyel Evrim (DE), DE optimization |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods. |
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