Dependent-Chance Programming Model for Stochastic Network Bottleneck Capacity Expansion Based on Neural Network and Genetic Algorithm - Wu, Yun; Zhou, Jian; Yang, Jun (Advances in Natural Computation, 2005)
This paper considers how to increase the capacities of the elements in a set E efficiently so that probability of the total cost for the increment of capacity can be under an upper limit to maximum extent while the final expansion capacity of a given family F of subsets of E is with a given limit bound. The paper supposes the cost w is a stochastic variable according to some distribution. Network bottleneck capacity expansion problem with stochastic cost is originally formulated as Dependent-chance programming model according to some criteria. For solving the stochastic model efficiently, network bottleneck capacity algorithm, stochastic simulation, neural network(NN) and genetic algorithm(GA) are integrated to produce a hybrid intelligent algorithm. Finally a numerical example is presented.