Сравнение методов

	Робастная муравьиная оптимизация	Устойчивый генетический алгоритм
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1992 (ACO); robust variants from ~2005	2005 (systematic survey); earlier applications from late 1990s
Автор метода	Dorigo, M. (ACO); robust extensions by multiple authors in 2000s–2010s	Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975)
Тип	Metaheuristic with robustness wrapper	Metaheuristic evolutionary optimizer with robustness mechanism
Основополагающий источник	Dorigo, M. (1992). Optimization, learning and natural algorithms. PhD Thesis, Politecnico di Milano, Italy.	Jin, Y., Branke, J. (2005). Evolutionary optimization in uncertain environments — a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317.
Другие названия	Robust ACO, Uncertainty-aware ACO, Min-max ACO, Robust ACO Metaheuristic	RGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm
Связанные	5	6
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.	The Robust Genetic Algorithm (RGA) extends standard genetic algorithms to find solutions that perform well not only at the nominal design point but also when subjected to uncertainty in decision variables, parameters, or fitness evaluations. By incorporating explicit robustness measures into selection pressure, RGA balances optimality against sensitivity to perturbation, making it suitable for engineering design, scheduling, and policy optimization under real-world variability.