We consider a new simulation-based optimization method called the Nested Partitions (NP) method. This method generates a Markov chain and solving the optimization problem is equiv...
In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and...
Anton J. Kleywegt, Alexander Shapiro, Tito Homem-d...
In this work we present a novel and efficient algorithm– independent stopping criterion, called the MGBM criterion, suitable for Multiobjective Optimization Evolutionary Algorit...
The need for a stopping criterion in MOEA’s is a repeatedly mentioned matter in the domain of MOOP’s, even though it is usually left aside as secondary, while stopping criteri...
This paper develops a variant of Simulated Annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be eva...