Robust topology optimization of truss structures with random loading and material properties: A multiobjective perspective

Richardson, James N.; Coelho, Rajan Filomeno; Adriaenssens, Sigrid

doi:10.1016/j.compstruc.2015.03.011

Cited by 51 publications

(28 citation statements)

References 22 publications

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“…The GA mimics the evolution of organisms, which selects individuals from the current generation as parents, generates new individuals as children by the crossover and mutation of the parents, and inherits better individuals to the next generation. In this study, the non‐dominated sorting genetic Algorithm II proposed by Deb et al is employed for exploration because this algorithm is effective and widespread employed for many optimization problems . Initially, a parent population P t = 1 with the size of N is created randomly.…”

Section: Methodsmentioning

confidence: 99%

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Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

Yoshimura

Shimoyama

Misaka

et al. 2016

Numerical Meth Engineering

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SUMMARYA non-gradient-based approach for topology optimization using a genetic algorithm is proposed in this paper. The genetic algorithm used in this paper is assisted by the Kriging surrogate model to reduce computational cost required for function evaluation. To validate the non-gradient-based topology optimization method in flow problems, this research focuses on two single-objective optimization problems, where the objective functions are to minimize pressure loss and to maximize heat transfer of flow channels, and one multiobjective optimization problem, which combines earlier two single-objective optimization problems. The shape of flow channels is represented by the level set function. The pressure loss and the heat transfer performance of the channels are evaluated by the Building-Cube Method code, which is a Cartesian-mesh CFD solver. The proposed method resulted in an agreement with previous study in the single-objective problems in its topology and achieved global exploration of non-dominated solutions in the multi-objective problems.

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Section: Methodsmentioning

confidence: 99%

“…In this study, the non-dominated sorting genetic algorithm II [18] proposed by Deb et al is employed for exploration because this algorithm is effective and widespread employed for many optimization problems [10,19]. Initially, a parent population P t D1 with the size of N is created randomly.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

Yoshimura

Shimoyama

Misaka

et al. 2016

Numerical Meth Engineering

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“…Then all the feasible design vectors can be classified into four groups based on their robust equilibrium coefficients corresponding to all the objective and constraint performance indices calculated by (13) and (21). Specifically, feasible design vector x belongs to Group A when ∀Bg k (x) ≥ 0 and ∀Bf ji (x) ≥ 0; it belongs to Group B when ∀Bg k (x) ≥ 0 but ∃Bf ji (x) < 0; it belongs to Group C when ∀Bf ji (x) ≥ 0 but ∃Bg k (x) < 0; it belongs to Group D when ∃Bg k (x) < 0 and ∃Bf ji (x) < 0.…”

Section: ) Grouping and Group Ranking Based On Robust Equilibrium Stmentioning

confidence: 99%

“…For example, Medina and Taflanidis [12] proposed a probabilistic robustness measure defined as the likelihood that a particular design outperformed the rival ones in an alternative set. Richardson et al [13] investigated the robust topology optimization of truss structures with random loads and material properties. Cheng et al [14] investigated the robust optimization of dynamical characteristics for complex structures with random parameters by integrating Kriging technique and the constrained non-dominated sorting genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous-Objective Robust Optimization of Complex Mechatronic Components Considering Interval Uncertainties

et al. 2019

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Structural optimization of complex mechatronic components may involve heterogeneous competing performance indices, including the cost, fixation, benefit, and deviation ones. However, such optimization problems with heterogeneous objectives have not been investigated so far. In this paper, a novel interval heterogeneous-objective robust optimization approach is proposed for complex mechatronic components. First, a unified interval heterogeneous-objective robust optimization model is constructed for mechatronic components with the uncertainties described as interval variables. Subsequently, a new interval robust equilibrium optimization algorithm is proposed to solve the interval heterogeneous-objective robust optimization model. Specifically, the unified formulas for assessing the robustness of interval heterogeneous-objective performance indices and the robust equilibrium among them are derived at first. Then, the preferential guidelines considering the robust equilibrium among all the objective and constraint performance indices are proposed for the direct ranking of various design vectors, and finally, the heterogeneous-objective robust equilibrium optimization of complex mechatronic components is realized by integrating the Kriging technique and the nested genetic algorithm. The feasibility and effectiveness of the proposed heterogeneous-objective robust optimization approach are verified by a numerical example and a case study. INDEX TERMS Heterogeneous-objective robust optimization, complex mechatronic component, interval, robust equilibrium, closeness coefficient, group ranking.

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“…In this study, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) [19] proposed by Deb et al is employed for exploration because this algorithm is effective and widespread employed for many optimization problems [9,20]. Initially, a parent population P t=1 with the size of N is created randomly.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Topology Optimization Using a Kriging-Assisted Genetic Algorithm With a Novel Level Set Representation Approach

Yoshimura¹,

Shimoyama

Misaka³

et al. 2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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Robust topology optimization of truss structures with random loading and material properties: A multiobjective perspective

Cited by 51 publications

References 22 publications

Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

Heterogeneous-Objective Robust Optimization of Complex Mechatronic Components Considering Interval Uncertainties

Topology Optimization Using a Kriging-Assisted Genetic Algorithm With a Novel Level Set Representation Approach

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