Structural Shape Optimization Using Evolution Strategies

Papadrakakis, Manolis; Tsompanakis, Yiannis; Lagaros, Nikos D.

doi:10.1080/03052159908941385

Cited by 52 publications

(32 citation statements)

References 12 publications

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“…On the other hand, probabilistic optimization techniques, such as Evolutionary Algorithms (EA), are robust and present a better global behaviour than the MP methods when dealing with complex optimization problems [14,15]. EA are not vulnerable to being trapped in local optima and therefore can be considered as reliable in approaching the global optimum for non-convex constrained optimization problems.…”

Section: Solving the Multi-objective Optimization Problemmentioning

confidence: 99%

“…Cascade optimization has been implemented using different deterministic optimizers [17], as well as using both deterministic and probabilistic optimizers [15,18]. The main advantage in combining different optimizers in a successive manner has to do with maximizing the exploitation of the advantages offered by various optimization algorithms and alleviating the influence of the corresponding disadvantages on the optimum design achieved.…”

Section: Augmented Weighted Tchebycheff Problemmentioning

confidence: 99%

“…Cascade optimization has been proposed as a remedy to this observation [15,17,18]. According to this optimization strategy, a multi-stage procedure is applied, in which various optimizers are implemented successively.…”

Section: Augmented Weighted Tchebycheff Problemmentioning

confidence: 99%

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Reliability based robust design optimization of steel structures

Lagaros

Plevris

Papadrakakis

2007

Int. J. Simul. Multidisci. Des. Optim.

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-In this work the uncertainty of a structural system is taken into account in the framework of a structural Reliability based Robust Design Optimization (RRDO) formulation where probabilistic constraints are incorporated into the robust design optimization formulation. A robust design optimization problem is formulated as a multi-criteria optimization problem. The Pareto front representing the solution of the RRDO problem is composed by designs with a state of robustness, since their performance is the least sensitive to the variability of the uncertain variables. The cross section dimensions together with other structural parameters, such as the modulus of elasticity, the yield stress and the applied loading, are considered as random variables. For the solution of the RRDO problem, the non-dominant Cascade Evolutionary Algorithm is employed combined with a weighted Tchebycheff metric.

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Section: Solving the Multi-objective Optimization Problemmentioning

confidence: 99%

Section: Augmented Weighted Tchebycheff Problemmentioning

confidence: 99%

See 1 more Smart Citation

Reliability based robust design optimization of steel structures

Lagaros

Plevris

Papadrakakis

2007

Int. J. Simul. Multidisci. Des. Optim.

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“…On the other hand metaheuristics, due to their random search, are being considered more robust in terms of global convergence; they may suffer, however, from a slow rate of convergence towards the global optimum. When metaheuristics are adopted to perform the optimization, the solution of the finite element equations is of paramount importance since more than 95% of the total computing time is spent for the solution of the finite element equilibrium equations [1]. A second characteristic is that in place of a single design point metaheuristics work simultaneously with a population of design points in the space of design variables.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Shape Design Optimization Into an Xfem Framework

Georgioudakis¹,

Lagaros²,

Papadrakakis³

2013

Proceedings of the 3rd South-East European Conference on Computational Mechanics – SEECCM III

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Abstract. Almost every real world problem involves simultaneous optimization of several incommensurable and often competing objectives which constitutes a multi-objective optimization problem. In multi-objective optimization problems the optimal solution is not unique as in single-objective optimization problems. In this paper, a multi-objective shape design optimization is implemented within the context of Extended Finite Element Method (XFEM

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“…The effectiveness of evolutionary type algorithms in structural shape optimization has been examined in a number of cases. In shape optimization of truss structures [1], aerodynamic improvement [2,3] and shape optimization of 2D mechanical parts [4,5]. Evolutionary algorithms have also been applied in a number of sizing structural optimization problems [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Soft computing methodologies for structural optimization

Papadrakakis

Lagaros

2003

Applied Soft Computing

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The paper examines the efficiency of soft computing techniques in structural optimization, in particular algorithms based on evolution strategies combined with neural networks, for solving large-scale, continuous or discrete structural optimization problems. The proposed combined algorithms are implemented both in deterministic and reliability based structural optimization problems, in an effort to increase the computational efficiency as well as the robustness of the optimization procedure. The use of neural networks was motivated by the time-consuming repeated finite element analyses required during the optimization process. A trained neural network is used to perform either the deterministic constraints check or, in the case of reliability based optimization, both the deterministic and the probabilistic constraints checks. The suitability of the neural network predictions is investigated in a number of structural optimization problems in order to demonstrate the computational advantages of the proposed methodologies.

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Structural Shape Optimization Using Evolution Strategies

Cited by 52 publications

References 12 publications

Reliability based robust design optimization of steel structures

Reliability based robust design optimization of steel structures

Multi-Objective Shape Design Optimization Into an Xfem Framework

Soft computing methodologies for structural optimization

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