Hybrid algorithms for handling the numerical noise in topology optimization

This paper presents new hybrid methods for the identification of optimal topologies by combining the teaching–learning based optimization (TLBO) and the method of moving asymptotes (MMA). The topology optimization problem is parameterizing with a low dimensional explicit method called moving morphable components (MMC), to make the use of evolutionary algorithms more efficient. Gradient-based solvers have good performance in solving large-scale topology optimization problems. However, in unconventional cases same as crashworthiness design in which there is numerical noise in the gradient information, the uses of these algorithms are unsuitable. The standard evolutionary algorithms can solve such problems since they don’t need gradient information. However, they have a high computational cost. This paper is based upon the idea of combining metaheuristics with mathematical programming to handle the probable noises and have faster convergence speed. Due to the ease of computations, the compliance minimization problem is considered as the case study and the artificial noise is added in gradient information.