3D topology optimization using an immune algorithm

Campelo, Felipe; Watanabe, Kazuhiro; Igarashi, Hajime

doi:10.1108/03321640710751145

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…[53], [52], [54] • Evolutionary algorithms with custom operators [64], [19], [68], [69], [89], [20] • Differential evolution [90], [91], [92] • Artificial immune systems [93], [94], [95], [96] • Hill climbing [48], [50] • Pattern search [59], [97] • Particle swarm optimization [47], [98], [99], [100] • Simulated annealing [101], [52], [102], [53], [51] • Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [61], [62], [63], [44], [103], [104], [105] • Ant colony optimization [106], [107], [108] • Constrained optimization by linear approximations (COBYLA) [55], [56], [109] • Random search [56], [109] • Structural stiffness and/or strength [75], [18], [21], …”

Section: Design Domain Representations Optimization Algorithms Applicationsmentioning

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: Design Domain Representations Optimization Algorithms Applicationsmentioning

confidence: 99%

Evolutionary Black-Box Topology Optimization: Challenges and Promises

Guirguis

Aulig

Picelli

et al. 2020

IEEE Trans. Evol. Computat.

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“…Plenty of different domain-specific optimization approaches have been proposed since then, based on genetic algorithms [22,43,103,196,203,205], artificial immune system algorithms [37,111], particle swarm optimization [110,112] or modified binary differential evolution [211]. Multiobjective genetic algorithms combined with local search operators have been applied [161,162], as well.…”

Section: Grid Representationmentioning

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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“…By reducing the constraint value for the shielding area at each optimization step, the effective topology of the magnetostatic shielding is computed. Only the genetic algorithm (GA) (Holland, 1975) had previously been implemented for the MS procedure; hence, a comparison with the immune algorithm (IA) (Farmer et al, 1986;Campelo et al, 2007) was drawn. Furthermore, an additional search (AS) in the restricted design space was added to the conventional MS procedure to produce a better solution for the next optimization step.…”

Section: Introductionmentioning

confidence: 99%

Topology optimization of magnetostatic shielding using multistep evolutionary algorithms with additional searches in a restricted design space

Okamoto

Tominaga

Wakao

et al. 2014

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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Purpose -The purpose of this paper is to improve the multistep algorithm using evolutionary algorithm (EA) for the topology optimization of magnetostatic shielding, and the paper reveals the effectiveness of methodology by comparison with conventional optimization method. Furthermore, the design target is to obtain the novel shape of magnetostatic shielding. Design/methodology/approach -The EAs based on random search allow engineers to define general-purpose objects with various constraint conditions; however, many iterations are required in the FEA for the evaluation of the objective function, and it is difficult to realize a practical solution without island and void distribution. Then, the authors proposed the multistep algorithm with design space restriction, and improved the multistep algorithm in order to get better solution than the previous one.Findings -The variant model of optimized topology derived from improved multistep algorithm is defined to clarify the effectiveness of the optimized topology. The upper curvature of the inner shielding contributed to the reduction of magnetic flux density in the target domain.Research limitations/implications -Because the converged topology has many pixel element unevenness, the special smoother to remove the unevenness will play an important role for the realization of practical magnetostatic shielding. Practical implications -The optimized topology will give us useful detailed structure of magnetostatic shielding. Originality/value -First, while the conventional algorithm could not find the reasonable shape, the improved multistep optimization can capture the reasonable shape. Second, An additional search is attached to the multistep optimization procedure. It is shown that the performance of improved multistep algorithm is better than that of conventional algorithm. IntroductionTopology optimization enables the design of novel geometries for electrical machines because the optimized solution does not depend on conventional information. Homogenization theory-based topology optimization was firstly proposed by Bendsøe and Kikuchi, who work in the research field of structural analysis (Bendsøe and Kikuchi, 1988). Subsequently, Bendsøe expanded homogenization-based method to the material density-based approach (Bendsøe, 1989), and its approach was introduced to the electromagnetic systems (Dyck and Lowther, 1996;Byun et al., 1999;Labbé and Dehez, 2010). The material density-based formulation permits the generation of intermediate material density, whereas the utilization of evolutionary algorithm (EA) has some possibility of searching a definite solution composed of binary-based material distribution without sensitivity computation of objective function.This paper proposes a topology optimization method for a magnetostatic shielding problem in order to reduce the magnetic flux density as much as possible. The design is achieved via multistep (MS) EAs in which the resolution is gradually improved using several kinds of finite element meshes. As a result, it has been ...

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3D topology optimization using an immune algorithm

Cited by 8 publications

References 24 publications

Evolutionary Black-Box Topology Optimization: Challenges and Promises

Evolutionary Black-Box Topology Optimization: Challenges and Promises

Generic Topology Optimization Based on Local State Features

Topology optimization of magnetostatic shielding using multistep evolutionary algorithms with additional searches in a restricted design space

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