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The objective of this paper is to investigate the efficiency of hybrid solution methods when incorporated into large‐scale optimization problems solved by evolution strategies (ESs) and to demonstrate their influence on the overall performance of these optimization algorithms. ESs imitate biological evolution and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism. In this paper modified multi‐membered evolution strategies with discrete variables are adopted. Two solution methods are implemented based on the preconditioned conjugate gradient (PCG) algorithm. The first method is a PCG algorithm with a preconditioner resulted from a complete Cholesky factorization, and the second is a PCG algorithm in which a truncated Neumann series expansion is used as a preconditioner. The numerical tests presented demonstrate the computational advantages of the proposed methods, which become more pronounced in large‐scale optimization problems and in a parallel computing environment.

Presents a pseudo‐elastic approach to topological optimization. In comparison with the well‐known homogenization method for topological optimization it is not based on a micro‐cellular structure, but approximates the elastic properties directly. A characteristic difficulty of these methods is the birth of new inner boundaries: thinning out the material can be interpreted as reducing the density of a composite micro‐structure, but eventually this process can result in a bubble with zero‐density. Therefore, the bubble‐method is a valuable asset to topological optimization, which helps to overcome this difficulty.