Global weight optimization of frame structures under free-vibration eigenvalue constraints

TL;DR

This paper develops a novel optimization framework using semidefinite programming and the Lasserre hierarchy to globally optimize the weight of frame structures under free-vibration eigenvalue constraints, ensuring convergence to optimality.

Contribution

It introduces a bilevel reformulation and a feasible point construction method that enable guaranteed bounds and convergence in global weight optimization of structures.

Findings

Hierarchy converges in at most five relaxation degrees

Provides guaranteed upper and lower bounds on the objective

Ensures convergence to the global optimum under convex minimizer sets

Abstract

Topology optimization of frame structures under free-vibration eigenvalue constraints constitutes a challenging nonconvex polynomial optimization problem with disconnected feasible sets. In this article, we first formulate it as a polynomial semidefinite programming problem (SDP) of minimizing a linear function over a basic semi-algebraic feasible set. We then propose to solve this problem by Lasserre hierarchy of linear semidefinite relaxations providing a sequence of increasing lower bounds. To obtain also a sequence of upper bounds and thus conditions on global $\varepsilon$-optimality, we provide a bilevel reformulation that exhibits a special structure: The lower level is quasiconvex univariate and it has a non-empty interior if the constraints of the upper-level problem are satisfied. After deriving the conditions for the solvability of the lower-level problem, we thus provide a way to construct feasible points to the original SDP. Using such a feasible point, we modify the original nonlinear SDP to satisfy the conditions for the deployment of the Lasserre hierarchy. Solving arbitrary degree relaxation of the hierarchy, we prove that scaled first-order moments associated with the problem variables satisfy feasibility conditions for the lower-level problem and thus provide guaranteed upper and lower bounds on the objective function. Using these bounds, we develop a simple sufficient condition for global $\varepsilon$-optimality and prove that the optimality gap $\varepsilon$ converges to zero if the set of global minimizers is convex. Finally, we illustrate these results with four representative problems for which the hierarchy converges in at most five relaxation degrees