Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Simulated Annealing× | Differentiaalikehitys – globaali stokastinen optimoija× | |
|---|---|---|
| Tieteenala | Optimointi | Optimointi |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1983 | 1997 |
| Kehittäjä≠ | — | Rainer Storn & Kenneth Price |
| Tyyppi≠ | Probabilistic metaheuristic / local search | Population-based stochastic metaheuristic |
| Alkuperäislähde≠ | Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. 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 ↗ |
| Rinnakkaisnimet | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search | DE algorithm, Diferansiyel Evrim (DE), DE optimization |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems. | 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. |
| ScholarGateAineisto ↗ |
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