方法对比
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| 蚁群优化× | 灰狼优化算法× | |
|---|---|---|
| 领域 | 优化 | 优化 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1992 (foundational thesis); 1997 (Ant Colony System formalization) | 2014 |
| 提出者≠ | — | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis |
| 类型≠ | Metaheuristic — swarm intelligence | Swarm-intelligence metaheuristic |
| 开创性文献≠ | 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 ↗ | Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗ |
| 别名 | ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | The Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space. |
| ScholarGate数据集 ↗ |
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