Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Abstract

This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms—denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)—is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines.

Time-optimal trajectory generation in joint space for 6R industrial serial robots using Cuckoo search algorithm

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Mechanical Characterization of Soft Membranes with One-Shot Projection Moiré and Metaheuristic Optimization

“…The blooming of metaheuristic methods favored by their inherent ability of finding global optima (or at least nearly global optimum solutions) as well as by the exponentially increasing computational power has led many researchers to use these algorithms in inverse problems. A comprehensive literature survey on applications of metaheuristic algorithms to mechanical characterization of materials is given in [27]. The interpretation of structural engineering from the perspective of inverse problems and the role played by metaheuristic algorithms and artificial intelligence in such a context are thoroughly reviewed in [28].…”

Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification

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