State-based representation for structural topology optimization and application to crashworthiness

Aulig, Nikola; Olhofer, Markus

doi:10.1109/cec.2016.7743985

Cited by 18 publications

(10 citation statements)

References 23 publications

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“…• Enhancement of the initial population [84], [25], [155], [142], [77] • Multi-stage and multi-population evolutionary algorithms [67], [79] • Clustering at the objective space [84], [85], [23], [156] • Sensitivity predictions and clustering in the state space [61], [157], [62], [63] • Fitness sharing and speciation [21], [158], [79] • Custom phenotype genetic operators [19], [50], [159], [32], [64], [82], [112], [27], [130] • Generative designs [160], [161] • Simple-to-complex evolutionary strategy [79]…”

Section: B Search Space Explorationmentioning

confidence: 99%

Evolutionary Black-Box Topology Optimization: Challenges and Promises

Guirguis

Aulig

Picelli

et al. 2020

IEEE Trans. Evol. Computat.

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Black-box topology optimization (BBTO) uses evolutionary algorithms and other soft computing techniques to generate near-optimal topologies of mechanical structures. Although evolutionary algorithms are widely used to compensate the limited applicability of conventional gradient optimization techniques, methods based on BBTO have been criticized due to numerous drawbacks. In this paper, we discuss topology optimization as a black-box optimization problem. We review the main BBTO methods, discuss their challenges and present approaches to relax them. Dealing with those challenges effectively can lead to wider applicability of topology optimization, as well as the ability to tackle industrial, highly-constrained, nonlinear, many-objective and multimodal problems. Consequently, future research in this area may open the door for innovating new applications in science and engineering that may go beyond solving classical optimization problems of structural engineering. Furthermore, algorithms designed for BBTO can be added to existing software toolboxes and packages of topology optimization.

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Section: B Search Space Explorationmentioning

confidence: 99%

Evolutionary Black-Box Topology Optimization: Challenges and Promises

Guirguis

Aulig

Picelli

et al. 2020

IEEE Trans. Evol. Computat.

Self Cite

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“…Cluster analysis has been implemented in structural optimization to reduce the computational cost of the optimization algorithm [21] and to reduce the dimension of the design space [4,35,36,37]. Using a modified p-norm distance, stress functions have been grouped to reduce the number of constraints in stress-constrained topology optimization [21].…”

Section: Md-19-1029mentioning

confidence: 99%

“…A clustering method in a genetic algorithm is also being reported for the design of rotor topologies [27,28]. Clustering in topology optimization has been utilized by researchers at the Honda Research Institute Europe GmbH to reduce the dimension in a multi-dimensional feature space [4].…”

Section: Md-19-1029mentioning

confidence: 99%

Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization

Detwiler

et al. 2019

Journal of Mechanical Design

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This work introduces a cluster-based structural optimization (CBSO) method for the design of categorical multimaterial structures subjected to crushing, dynamic loading. The proposed method consists of three steps: conceptual design generation, design clustering, and Bayesian optimization. In the first step, a conceptual design is generated using the hybrid cellular automaton (HCA) algorithm. In the second step, threshold-based cluster analysis yields a lower-dimensional design. Here, a cluster validity index for structural optimization is introduced in order to qualitatively evaluate the clustered design. In the third step, the optimal design is obtained through Bayesian optimization, minimizing a constrained expected improvement function. This function allows to impose soft constraints by properly redefining the expected improvement based on the maximum constraint violation. The Bayesian optimization algorithm implemented in this work has the ability to search over (i) a real design space for sizing optimization, (ii) a categorical design space for material selection, or (iii) a mixed design space for concurrent sizing optimization and material selection. With the proposed method, materials are optimally selected based on multiple attributes and multiple objectives without the need for material ranking. The effectiveness of this approach is demonstrated with the design for crashworthiness of multimaterial plates and thin-walled structures.

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“…Comparisons to a state-of-the-art uniform energy heuristic are presented. Parts of this chapter are based on [10,13].…”

Section: Topology Optimization Of Crashworthiness Objectivesmentioning

confidence: 99%

Generic Topology Optimization Based on Local State Features

Aulig¹

2017

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The work at hand addresses engineers, designers and scientists who face the challenging Task of devising concept structures in a virtual product design process that involves more and more sophisticated physical simulations. Using methods of evolutionary optimization and machine learning, this dissertation explores a novel generic topology optimization algorithm, which is able to provide concept designs even for problems involving complex, black-box simulations. A self-contained learning component utilizes physical simulation data to generate a search direction. The generic topology optimization is studied in conjunction with statistical models such as neural networks or support vector regression. In empirical experiments, the novel method reproduces reference structures with Minimum compliance and provides innovative solutions in the domain of vehicle crashworthiness optimization. Contents Symbols and Abbreviations XIV Abstract XVII Zusammenfassung XVIII 1 Introduction 1 2 Fundamentals...

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State-based representation for structural topology optimization and application to crashworthiness

Cited by 18 publications

References 23 publications

Evolutionary Black-Box Topology Optimization: Challenges and Promises

Evolutionary Black-Box Topology Optimization: Challenges and Promises

Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization

Generic Topology Optimization Based on Local State Features

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