Improved genetic algorithm with two-level approximation for truss topology optimization

Truss topology optimization using Genetic Algorithms (GAs) usually requires large computational cost, especially for large-scale problems. To decrease the structural analyses, a GA with a Two-level Approximation (GATA) was proposed in a previous work, and showed good computational efficiency with less structural analyses. However, this optimization method easily converges to sub-optimum points, resulting in a poor ability to search for a global optimum. Therefore, to address this problem, we propose an Improved GA with a Two-level Approximation (IGATA) which includes several modifications to the approximation function and simple GA developed previously. A Branched Multi-point Approximation (BMA) function, which is efficient and without singularity, is introduced to construct a first-level approximation problem. A modified Lemonge penalty function is adopted for the fitness calculation, while an Elite Selection Strategy (ESS) is proposed to improve the quality of the initial points. The results of numerical examples confirm the lower computational cost of the algorithm incorporating these modifications. Numerous numerical experiments show good reliability of the IGATA given appropriate values for the considered parameters.