An Effective Intelligent Algorithm for Stochastic Optimization Problem
Stochastic optimization problems widely exist in engineering, management, control and many other fields. In order to search a more effective algorithm for solving these problems, generalized regression neural network is used as a fitness prediction model and an intelligent algorithm which combines generalized regression neural network with particle swarm optimization is presented. In this intelligent algorithm, according to the mechanism combined prediction model with particle swarm optimization and prediction strategy, some of the individuals fitness is predicted and the rest is estimated by random simulation. Results of simulations show that the algorithm reduces the computational cost greatly in the premise of performance guarantee.
stochastic optimization problem random simulation generalized regression neural network particle swarm optimization
CUI Fang-shu ZENG Jian-chao
Institute of System Simulation & Computer Application, Taiyuan University of Science & Technology, Taiyuan 030024, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
3197-3202
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）