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Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures

In this study the computational performance of adaptive evolution strategies (ESs) in large-scale structural optimization is mainly investigated to achieve the following objectives: (i) to present an ESs based solution algorithm for efficient optimum design of large structural systems consisting of continuous, discrete and mixed design variables; (ii) to integrate new parameters and methodologies into adaptive ESs to improve the computational performance of the algorithm; and (iii) to assess successful self-adaptation models of ESs in continuous and discrete structural optimizations. A numerical example taken from the literature is studied in depth to verify the enhanced performance of the algorithm, as well as to scrutinize the role and significance of self-adaptation in ESs for a successfully implemented optimization process. Besides, the utility of the algorithm for practical structural engineering applications is demonstrated using a bridge design example. It is shown that adaptive ESs are reliable and powerful tools, and well-suited for optimum design of complex structural systems, including large-scale structural optimization.