Robust-Optimal Design of a Lightweight Space Structure Using a Genetic Algorithm

Anthony, David K.; Keane, Andy J.

doi:10.2514/2.2114

Cited by 23 publications

(11 citation statements)

References 3 publications

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“…Besides, a major problem with many existing robust schemes in the literature is that the nominal fitness of the final design is often neglected [3,5,12]. These schemes generally optimize the robustness of the final design, at the expense of nominal performance of the final design.…”

Section: Evolutionary Design Optimization In the Presence Of Uncertaintymentioning

confidence: 98%

“…In many real world engineering design problems [12,[14][15][16], it is often the case that very little knowledge about the structure of the uncertainty involved is available. Hence, instead of focusing on making any probably unjustifiable mathematical model out of the uncertainty, we focus here on how a design may deteriorate in the presence of uncertainties.…”

Section: Inverse Multi-objective Robust Evolutionary Design Optimizationmentioning

confidence: 99%

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Inverse multi-objective robust evolutionary design optimization in the presence of uncertainty

Lim

Ong

Lee

2005

Proceedings of the 7th Annual Workshop on Genetic and Evolutionary Computation

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In many real-world design problems, uncertainties are often present and practically impossible to avoid. Many existing works on Evolutionary Algorithm (EA) for handling uncertainty have emphasized on introducing some prior structure of the uncertainty or noise to the variable domain and conducting sensitivity analysis based on the assumed information. In this paper, we present an evolutionary design optimization that handles the presence of uncertainty with respect to the desired robust performance in mind, which we call an inverse robust design. The scheme, unlike others developed to represent uncertainty does not assume any structure of the uncertainty involved; hence it is particularly useful when there is very little information about the uncertainties available. In our formulation, we model the clustering of uncertain events in families of nested sets using a multi-level optimization searches within the multi-objective evolutionary search. Empirical studies were conducted on synthetic functions to demonstrate that our algorithm converges to a set of designs with non-dominated nominal performances and robustness to the presence of uncertainties.Evolutionary Algorithms, robust design optimization, design optimization in the presence of uncertainty.

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Section: Evolutionary Design Optimization In the Presence Of Uncertaintymentioning

confidence: 98%

Section: Inverse Multi-objective Robust Evolutionary Design Optimizationmentioning

confidence: 99%

Inverse multi-objective robust evolutionary design optimization in the presence of uncertainty

Lim

Ong

Lee

2005

Proceedings of the 7th Annual Workshop on Genetic and Evolutionary Computation

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“…The probabilistic-based design optimization methodologies can be classified in the following formulations: robust design optimization (RDO) (Anthony and Keane, 2003;Beyer and Sendhoff, 2007;Doltsinis et al, 2005;Lagaros and Papadrakakis, 2007;McAllister and Simpson, 2003;Park et al, 2006;Parkinson et al, 2007;Rangavajhala et al, 2007;Ray and Smith, 2006;Schumacher and Olschinka, 2008;Zang et al, 2005) and reliability-based design optimization (RBO) (Acar and Haftka, 2007;Agarwal and Renaud, 2004;Du et al, 2006;Frangopol and Maute, 2003;Gunawan and Papalambros, 2006;Jensen et al, 2008;Jiang et al, 2000;Lagaros et al, 2008;Liang et al, 2007;Moses, 1997;Nikolaidis, 2007;Pu et al, 1997;Rackwitz, 2004;Repalle and Grandhi, 2005;Royset et al, 2001;Sørensen and TarpJohansen, 2005;Streicher and Rackwitz, 2004;Taflanidis and Beck, 2008). RDO methods primarily seek to minimize the influence of random variations of the nominal structural dimensions, material parameters and loading on the response of the structure.…”

Section: Introductionmentioning

confidence: 99%

Neurocomputing strategies for solving reliability‐robust design optimization problems

Lagaros

Plevris²,

Papadrakakis³

2010

Engineering Computations

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Purpose -This paper, by taking randomness and uncertainty of structural systems into account aims to implement a combined reliability-based robust design optimization (RRDO) formulation. The random variables to be considered include the cross section dimensions, modulus of elasticity, yield stress, and applied loading. The RRDO problem is to be formulated as a multi-objective optimization problem where the construction cost and the standard deviation of the structural response are the objectives to be minimized. Design/methodology/approach -The solution of the optimization problem is performed with the non-dominant cascade evolutionary algorithm with the weighted Tchebycheff metric, while the probabilistic analysis required is carried out with the Monte Carlo simulation method. Despite the computational advances, the solution of a RRDO problem for real-world structures is extremely computationally demanding and for this reason neurocomputing estimations are implemented. Findings -The obtained estimates with the neural network predictions are shown to be very satisfactory in terms of accuracy for performing this type of computation. Furthermore, the present numerical results manage to achieve a reduction in computational time up to four orders of magnitude, for low probabilities of violation, compared to the conventional procedure making thus feasible the reliability-robust design optimization of realistic structures under probabilistic constraints. Originality/value -The novel parts of the present work include the implementation of neurocomputing strategies in RRDO problems for reducing the computational cost and the comparison of the results given by RRDO and robust design optimization formulations, where the significance of taking into account probabilistic constraints is emphasized.

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“…To improve the robustness of GA, various schemes have been incorporated into GA, such as computer experiment methods, noisy phenotype methods [8]. Almost all of them are based on incorporating minor perturbations to the configurations evolved by the GA to assess their robustness, resulting intensive computational cost.…”

Section: Introductionmentioning

confidence: 99%

Robust optimal trajectory design by integrating genetic algorithm with min-max method

Song

Chen

Li³

et al. 2011

Int. J. Control Autom. Syst.

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A robust optimal trajectory design method is proposed in this paper. Genetic Algorithm (GA) is employed to optimize the whole trajectory to improve the terminal attack performance. To enhance the robustness of the trajectory to disturbances, the min-max method is integrated into the GA optimization process. The proposed approach is carefully illustrated with the robust optimization design of the trajectory for a portable short-range top-attack (PSRTA) missile. The H ∞ robust gainscheduled technique is used to design the attitude tracking autopilot to facilitate the trajectory design. The damp feedback loop of the weakly-damped missile body is innovatively treated as the Linear Parameter Varying system (the controlled plant), which is good for practical use. The proposed robust optimal trajectory design method and the H ∞ robust gain-scheduled attitude tracking autopilot are demonstrated to be effective from the whole trajectory simulation results of the PSRTA missile, which also exhibit high applicability for practical engineering problems.

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Robust-Optimal Design of a Lightweight Space Structure Using a Genetic Algorithm

Cited by 23 publications

References 3 publications

Inverse multi-objective robust evolutionary design optimization in the presence of uncertainty

Inverse multi-objective robust evolutionary design optimization in the presence of uncertainty

Neurocomputing strategies for solving reliability‐robust design optimization problems

Robust optimal trajectory design by integrating genetic algorithm with min-max method

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