Dependent-Chance Programming Model for Stochastic Network Bottleneck Capacity Expansion Based on Neural Network and Genetic Algorithm

• Published in: Advances in Natural Computation pp. 120 - 128
• Main Authors: Wu, Yun, Zhou, Jian, Yang, Jun
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Capacity Expansion; Computer science; control theory; systems; Exact sciences and technology; Expansion Problem; Genetic Algorithm; Polynomial Algorithm; Stochastic Simulation; Bottleneck; Upper bound; Probabilistic approach; Genetic algorithm; Stochastic simulation; Neural network; Randomized algorithm; Modeling; Stochastic programming
• ISBN: 9783540283201; 354028320X; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539902_14

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.