Abstract
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.
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