عنوان مقاله [English]
Transmission towers are the most important part of the systems for transferring electrical power and for its distribution. These
structures are designed to support the conductors and ground wires of electrical power lines and withstand mechanical forces in the worst weather and under the worst atmospheric conditions. A linear analysis is generally sufficient for the analysis of a transmission tower. Since these structures contain a high number of elements, and due to their high cost, their optimization is of great importance from both a material and a construction point of view. Transmission towers are often subjected to many combinations of loading and, in the process of optimization, one is faced with many analyses. In this paper, a genetic algorithm is employed for optimization. One of the main difficulties in using the genetic algorithm for optimization is that, for each chromosome in each generation, one analysis should be performed. This requires the inversion of matrices and, with an increase in the number
of elements and in geometric optimization, the convergence becomes very slow. In order to overcome this difficulty, neural networks are trained as analyzers to take on part of the computational load. Using neural networks increases the rate of convergence of the optimization. A multi-population genetic algorithm is used in this article.