A conservative method of wavelet neural network based meta-modeling in constrained approximate optimization
The paper aims at the development of the wavelet neural network (WNN) based conservative meta-model that satisfies the constraint feasibility of approximate optimal solution. The WNN based constraint-feasible meta-model is formulated via exterior penalty method to optimally determine interconnection weights and dilation and translation coefficients in the network. Using Ackleys path function, the approximation performance of WNN is first tested in comparison with BPN. The proposed approach of constraint feasibility is then verified through a ten-bar planar truss problem. For constrained approximate optimization, the structural design of a composite rotor blade is explored to support the proposed strategies.
wavelet neural network constrained approximate optimization constraint feasibility inequality constraints