Порівняння методів

	Симгіристика: поєднання симуляції з метагіристикою для стохастичної оптимізації	Стохастична оптимізація
Галузь	Оптимізація	Оптимізація
Родина	Process / pipeline	Process / pipeline
Рік появи	2015	1951 (SGD); 2014 (Adam)
Автор методу	Juan et al.	—
Тип	Hybrid simulation-optimization framework	Gradient-based iterative optimization
Основоположне джерело	Juan, A. A., et al. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72.	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407.
Інші назви	Simulation-based Metaheuristics, Stochastic Metaheuristics with Simulation, Hybrid Simulation-Optimization, Simülistik Sezgiseller	Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam
Пов'язані	3	3
Підсумок	Simheuristics is a hybrid algorithmic framework that integrates Monte Carlo or discrete-event simulation into metaheuristic search procedures to solve stochastic combinatorial optimization problems. Introduced by Juan et al. in 2015, it addresses settings where objective function evaluations involve random variables, providing near-optimal solutions with probabilistic quality guarantees. The approach is especially suited for real-world logistics, transportation, and scheduling problems where uncertainty is inherent and classical deterministic solvers fail to capture variability.	Stochastic optimization is a family of iterative methods that minimize an objective function by computing gradients on randomly sampled subsets of data — mini-batches — rather than on the entire dataset at once. Pioneered by Robbins and Monro in 1951 as stochastic approximation, the approach became the standard engine for training large-scale machine-learning models through variants such as SGD with momentum, AdaGrad, RMSProp, and Adam.