Optimal Design of Transmission Towers Using Genetic Algorithm and Neural Networks

Abstract

In the context of transmission tower optimization, the energy method and the force method are combined in order to form a holistic design and optimization approach, eliminating the need for time-intensive matrix inversion. A migration genetic algorithm is employed in the optimization process. Although this algorithm is suitable for towers with a limited number of elements, it is inefficient in the case of many towers encountered in practice. The addition of a neural network as an analysis tool reduces the overall computational load. Four examples are presented to demonstrate the important role of neural networks in reducing the computational overhead.