Dynamic Filled Algorithm for Global Optimization of Nonlinear Programming
Combined the advantage of Hopfield neural network and filled function method, a dynamic filled algorithm will be presented for constrainted global optimization of nonlinear programming. The algorithm contains two phases. The dynamic minimizing phase in which the dynamic minimizing system is used to find the local minimizer of the global optimization. And in the dynamic filled phase, a new initial condition in a lower basin can be determined by the dynamic filled system. By repeating two dynamic systems of the algorithm, a global minimal point can be obtained at last. The algorithm not only makes the computation simple, rapid, and criterion, but also prevents the Hopfield neural network from getting trapped in the local minima.
Neural network Filled function Differential dynamical systems Nonlinear programming Global optimization.
Wenwen Ma Jie Song Wei Wang
Department of Mathematics East China University of Science & Technology Shanghai China
国际会议
昆明
英文
23-27
2010-10-17（万方平台首次上网日期，不代表论文的发表时间）