An SLP Algorithm for Topology Optimization

Topology optimization problems, in general, and compliant mechanism design problems, in particular, are engineering applications that rely on nonlinear programming algorithms. Since these problems are usually huge, methods that do not require information about second derivatives are generally used for their solution. The most widely used of such methods are some variants of the method of moving asymptotes (MMA), proposed by Svanberg (1987), and sequential linear programming (SLP). Although showing a good performance in practice, most of the SLP algorithms used in topology optimization lack a global convergence theory. This paper introduces a globally convergent SLP method for nonlinear programming. The algorithm is applied to the solution of classic compliance minimization problems, as well as to the design of compliant mechanisms. Our numerical results suggest that the new algorithm is faster than the globally convergent version of the MMA method.