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| 베이지안 개미 군집 최적화× | 개미 군집 최적화× | |
|---|---|---|
| 분야≠ | 시뮬레이션 | 최적화 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1996 (ACO); Bayesian variant: 2000s | 1992 (foundational thesis); 1997 (Ant Colony System formalization) |
| 창시자≠ | Dorigo, M. et al. (ACO); Bayesian extensions by multiple researchers in the 2000s–2010s | — |
| 유형≠ | Metaheuristic with Bayesian probabilistic learning | Metaheuristic — swarm intelligence |
| 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 ↗ | Dorigo, M. & Gambardella, L.M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66. DOI ↗ |
| 별칭≠ | BACO, Bayesian ACO, Bayesian-guided ACO, Probabilistic ACO | ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system |
| 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. | Ant Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulating the collective foraging behaviour of ants. Real ants lay pheromone trails on paths and preferentially follow stronger trails; ACO turns this positive-feedback mechanism into a search procedure that finds high-quality solutions to graph-structured problems such as the Travelling Salesman Problem, vehicle routing, and scheduling. |
| ScholarGate데이터셋 ↗ |
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