Process / pipelineSimulation / optimization
鲁棒蚁群优化——不确定性弹性ACO在组合问题中的应用
鲁棒蚁群优化（Robust Ant Colony Optimization, Robust ACO）通过显式地将参数不确定性以及最坏情况或期望情况的鲁棒性准则纳入解决方案搜索中，扩展了经典的蚁群元启发式算法。它不是针对单一标称情景进行优化，而是寻求在各种可能的实际问题情景中表现良好的解决方案，使其适用于输入数据（成本、需求、旅行时间）不确定或可变的现实世界组合问题。
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来源
- Dorigo, M. (1992). Optimization, learning and natural algorithms. PhD Thesis, Politecnico di Milano, Italy. link ↗
- Gutjahr, W. J., & Pflug, G. C. (2010). Simulated annealing for noisy cost functions. Journal of Global Optimization, 12(2), 123–147. (For robust stochastic metaheuristics including ACO under uncertainty.) link ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Ant Colony Optimization — ACO metaheuristic with explicit uncertainty and worst-case robustness handling. ScholarGate. https://scholargate.app/zh/simulation/robust-ant-colony-optimization
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