Two general-purpose metaheuristic algorithms for solving multiobjective stochastic combinatorial optimization problems are introduced: SP-ACO (based on the Ant Colony Optimization ...
In this paper, we study the relationship between the two techniques known as ant colony optimization (aco) and stochastic gradient descent. More precisely, we show that some empir...
To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision...
Abstract. Stochastic optimization is a leading approach to model optimization problems in which there is uncertainty in the input data, whether from measurement noise or an inabili...
The algorithmic framework developed for improving heuristic solutions of the new version of deterministic TSP [Choi et al., 2002] is extended to the stochastic case. To verify the...