Designing airfoils using a reference point based evolutionary many-objective particle swarm optimization algorithm

Wickramasinghe, Upali K.; Carrese, Robert; Li, Xiaodong

doi:10.1109/cec.2010.5586221

Cited by 56 publications

(32 citation statements)

References 16 publications

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“…To the best of our knowledge, Wickramasinghe et al [8] were the first to propose a metric for comparing userpreference based EMO algorithms. This metric works by combining the solution sets of all algorithms that need to be compared.…”

Section: Related Workmentioning

confidence: 99%

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A new performance metric for user-preference based multi-objective evolutionary algorithms

Mohammadi

Omidvar

2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-In this paper, we propose a metric for evaluating the performance of user-preference based evolutionary multiobjective algorithms by defining a preferred region based on the location of a user-supplied reference point. This metric uses a composite front which is a type of reference set and is used as a replacement for the Pareto-optimal front. This composite front is constructed by extracting the non-dominated solutions from the merged solution sets of all algorithms that are to be compared. A preferred region is then defined on the composite front based on the location of a reference point. Once the preferred region is defined, existing evolutionary multi-objective performance metrics can be applied with respect to the preferred region. In this paper the performance of a cardinality-based metric, a distance-based metric, and a volume-based metric are compared against a baseline which relies on knowledge of the Pareto-optimal front. The experimental results show that the distance-based and the volume-based metrics are consistent with the baseline, showing meaningful comparisons. However, the cardinality-based approach shows some inconsistencies and is not suitable for comparing the algorithms.

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Section: Related Workmentioning

confidence: 99%

“…A metric which has been recently developed by Wickramasinghe et al [8] is specifically designed for comparing user-preference based EMO algorithms. However, a major drawback of this metric is that its results can be misleading, depending on the choice of the reference point (cf.…”

Section: Introductionmentioning

confidence: 99%

A new performance metric for user-preference based multi-objective evolutionary algorithms

Mohammadi

Omidvar

2013

2013 IEEE Congress on Evolutionary Computation

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“…In addition, real-world design problems often involve several objective functions [1,2]. For example, in aircraft design, it is necessary for a designer to account for not only the performance at a specific cruise condition, but also the performances at all operating speeds, including those during take-off and landing.…”

Section: Introductionmentioning

confidence: 99%

Multi-Fidelity Multi-Objective Efficient Global Optimization Applied to Airfoil Design Problems

Ariyarit

Kanazaki

2017

Applied Sciences

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Abstract:In this study, efficient global optimization (EGO) with a multi-fidelity hybrid surrogate model for multi-objective optimization is proposed to solve multi-objective real-world design problems. In the proposed approach, a design exploration is carried out assisted by surrogate models, which are constructed by adding a local deviation estimated by the kriging method and a global model approximated by a radial basis function. An expected hypervolume improvement is then computed on the basis of the model uncertainty to determine additional samples that could improve the model accuracy. In the investigation, the proposed approach is applied to two-objective and three-objective optimization test functions. Then, it is applied to aerodynamic airfoil design optimization with two objective functions, namely minimization of aerodynamic drag and maximization of airfoil thickness at the trailing edge. Finally, the proposed method is applied to aerodynamic airfoil design optimization with three objective functions, namely minimization of aerodynamic drag at cruising speed, maximization of airfoil thickness at the trialing edge and maximization of lift at low speed assuming a landing attitude. XFOILis used to investigate the low-fidelity aerodynamic force, and a Reynolds-averaged Navier-Stokes simulation is applied for high-fidelity aerodynamics in conjunction with a high-cost approach. For comparison, multi-objective optimization is carried out using a kriging model only with a high-fidelity solver (single fidelity). The design results indicate that the non-dominated solutions of the proposed method achieve greater data diversity than the optimal solutions of the kriging method. Moreover, the proposed method gives a smaller error than the kriging method.

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“…A reference point, which is specified as an array of m aspiration values, is projected onto the Pareto landscape by the designer to guide the swarm toward solutions of interest. Unlike goal attainment methods, which make explicit reference to a target design [13], the reference point is a means of expressing the designer's preferred level of compromise, which can ideally be based on an existing or target design [14][15][16]. The swarm is guided by this information to confine its search to the preferred region of the Pareto front in the vicinity of the reference point.…”

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confidence: 99%

“…The integration of the reference point method has proven to overcome many limitations that plague conventional evolutionary algorithms, including many-objective problems [16,17]. Of particular significance is the ability to converge over large multimodal design spaces (which are typical of engineering design problems) and precision in the exploitation of individual solutions [14,15].…”

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confidence: 99%

Swarm Heuristic for Identifying Preferred Solutions in Surrogate-Based Multi-Objective Engineering Design

Carrese¹,

Sóbester

Winarto³

et al. 2011

AIAA Journal

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Exploring the entire Pareto frontier of high-fidelity multidisciplinary problems can be prohibitive due to the excessive number of expensive evaluations required. The use of surrogate models offers promise toward managing such problems, which are restricted by a computational budget. In this paper, the kriging-assisted user-preference multi-objective particle swarm heuristic is presented, in which less accurate but inexpensive surrogate models are used cooperatively with the precise but expensive objective functions to alleviate the computational burden. A userpreference module is integrated into the optimization framework, which guides the swarm toward preferred regions of the Pareto frontier, thereby focusing all computing effort on identifying only solutions of interest to the designer. While providing a logical criterion to prescreen candidates for precise evaluation, the additional guidance provided by user-preferences guarantees an accelerated convergence rate. To depict the proficiency of the proposed framework, a suite of test problems, including the multidisciplinary cross-sectional design of a semimonocoque fuselage enclosing a pressurized cabin and payload bay, is presented. A parametric model is described that is capable of generating a broad range of double-lobe fuselage designs. The superiority of the kriging-assisted user-preference multi-objective particle swarm optimization algorithm over more traditional search methods to efficiently manage high-fidelity discontinuous design problems is highlighted.

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Designing airfoils using a reference point based evolutionary many-objective particle swarm optimization algorithm

Cited by 56 publications

References 16 publications

A new performance metric for user-preference based multi-objective evolutionary algorithms

A new performance metric for user-preference based multi-objective evolutionary algorithms

Multi-Fidelity Multi-Objective Efficient Global Optimization Applied to Airfoil Design Problems

Swarm Heuristic for Identifying Preferred Solutions in Surrogate-Based Multi-Objective Engineering Design

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