Stochastic Optimization Problem through Particle Swarm Optimization Algorithm

Abstract

In this paper, two classes of stochastic optimization problems, which are expected value models and chance-constrained programming, are introduced. In order to solve the problems, the method of stochastic simulation is used to generate training samples for neural network, and then particle swarm optimization algorithm and neural network are integrated to produce a hybrid intelligent algorithm. Two numerical examples are provided to illustrate the effectiveness of the hybrid particle swarm optimization algorithm.