Сравнение методов

	Робастная муравьиная оптимизация	Муравьиные алгоритмы
Область	Имитационное моделирование	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Год появления	1992 (ACO); robust variants from ~2005	1992 (foundational thesis); 1997 (Ant Colony System formalization)
Автор метода	Dorigo, M. (ACO); robust extensions by multiple authors in 2000s–2010s	—
Тип	Metaheuristic with robustness wrapper	Metaheuristic — swarm intelligence
Основополагающий источник	Dorigo, M. (1992). Optimization, learning and natural algorithms. PhD Thesis, Politecnico di Milano, Italy.	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.
Другие названия	Robust ACO, Uncertainty-aware ACO, Min-max ACO, Robust ACO Metaheuristic	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system
Связанные	5	5
Сводка	Robust Ant Colony Optimization (Robust ACO) extends the classic ant colony metaheuristic by explicitly incorporating parameter uncertainty and worst-case or expected-case robustness criteria into the solution search. Rather than optimizing for a single nominal scenario, it seeks solutions that perform well across a range of plausible problem realizations, making it suitable for real-world combinatorial problems where input data (costs, demands, travel times) are uncertain or variable.	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.