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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.