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| 베이지안 개미 군집 최적화× | 베이지안 시뮬레이티드 어닐링× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1996 (ACO); Bayesian variant: 2000s | 1984 |
| 창시자≠ | Dorigo, M. et al. (ACO); Bayesian extensions by multiple researchers in the 2000s–2010s | Geman, S. & Geman, D. (Bayesian framing); Kirkpatrick, S. et al. (SA foundation) |
| 유형≠ | Metaheuristic with Bayesian probabilistic learning | Probabilistic metaheuristic with Bayesian inference |
| 원전≠ | Dorigo, M., Maniezzo, V., Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26(1), 29–41. DOI ↗ | Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. DOI ↗ |
| 별칭 | BACO, Bayesian ACO, Bayesian-guided ACO, Probabilistic ACO | BSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic Optimization |
| 관련 | 5 | 5 |
| 요약≠ | Bayesian Ant Colony Optimization (BACO) is a hybrid metaheuristic that embeds Bayesian inference into the Ant Colony Optimization framework. By treating pheromone intensities or algorithm parameters as probability distributions updated with collected evidence, BACO improves convergence reliability and robustness compared to classical ACO on noisy or uncertain combinatorial optimization problems. | Bayesian Simulated Annealing (BSA) integrates Bayesian prior knowledge about the objective landscape into the simulated annealing search process. By encoding beliefs about promising regions as prior distributions and updating them as the search progresses, BSA focuses computational effort on high-probability areas of the solution space, accelerating convergence and improving solution quality compared to uninformed SA. |
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