Evaluating Multi-criteria Evolutionary Algorithms for Airfoil Optimisation

Evolutionary (multi-objective optimization) algorithms (EMOAs) are widely accepted to be competitive optimization methods in industry today. However, normally only standard techniques are employed by the engineering experts. Here, it is shown how these standard techniques can be completed and improved with respect to interactivity to other tools, runtime, and parameterization. The coupling with metamodels serves as an example for the interactivity to other tools, while the online convergence detection relates to runtime, i.e. stopping criteria. Finally, sequential parameter optimization improves results focussing on parameter tuning. We show that invoking all these methods on their own already enhances EMOAs for aerodynamic applications. It is concluded with an outlook on how these methods might come together to foster aerospace applications and, at a time, widen the application area to multi-disciplinary optimization tasks.

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“…For reasons of comparability, a method to average Pareto fronts from reference [23] is utilized. In the lefthand part of Fig.…”

Section: Resultsmentioning

confidence: 99%

Advanced concepts for multi-objective evolutionary optimization in aircraft industry

Naujoks

Trautmann

Wessing

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Although this was a very small number of objective function evaluations, the authors reported the generation of good approximations of the Pareto front. In a further paper, Naujoks et al [114] proposed to use a (20+20)-MODES, along with an additional selection mechanism inspired on the NSGA-II. The results presented with this additional selection mechanism were very similar to those obtained before, both in terms of quality of the Pareto approximation and in terms of the spread of the nondominated solutions along the Pareto front.…”

Section: B 2-d Geometries and Airfoil Shape Optimizationmentioning

confidence: 98%

Multiobjective Evolutionary Algorithms in Aeronautical and Aerospace Engineering

Arias-Montaño

Coello

Mezura-Montes

2012

IEEE Trans. Evol. Computat.

127

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Nowadays, the solution of multiobjective optimization problems in aeronautical and aerospace engineering has become a standard practice. These two fields offer highly complex search spaces with different sources of difficulty, which are amenable to the use of alternative search techniques such as metaheuristics, since they require little domain information to operate. From the several metaheuristics available, multiobjective evolutionary algorithms (MOEAs) have become particularly popular, mainly because of their availability, ease of use, and flexibility. This paper presents a taxonomy and a comprehensive review of applications of MOEAs in aeronautical and aerospace design problems. The review includes both the characteristics of the specific MOEA adopted in each case, as well as the features of the problems being solved with them. The advantages and disadvantages of each type of approach are also briefly addressed. We also provide a set of general guidelines for using and designing MOEAs for aeronautical and aerospace engineering problems. In the final part of the paper, we provide some potential paths for future research, which we consider promising within this area.

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“…Since then many example problems have been published. For instance, Naujoks et al presented a multi-objective shape optimisation problem for aerofoils, optimising high-lift and low-drag simultaneously using CFD simulations [2]. Similar problems with a particular attention to drag coefficients and uncertainty are available from [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Suite of Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics

Daniels

Rahat

Everson

et al. 2018

Parallel Problem Solving From Nature – PPSN XV

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In many product design and development applications, Computational Fluid Dynamics (CFD) has become a useful tool for analysis. This is particularly because of the accuracy of CFD simulations in predicting the important flow attributes for a given design. On occasions when design optimisation is applied to real-world engineering problems using CFD, the implementation may not be available for examination. As such, in both the CFD and optimisation communities, there is a need for a set of computationally expensive benchmark test problems for design optimisation using CFD. In this paper, we present a suite of three computationally expensive real-world problems observed in different fields of engineering. We have developed Python software capable of automatically constructing geometries from a given decision vector, running appropriate simulations using the CFD code OpenFOAM, and returning the computed objective values. Thus, users may easily evaluate a decision vector and perform optimisation of these design problems using their optimisation methods without developing custom CFD code. For comparison, we provide the objective values for the base geometries and typical computation times for the test cases presented here.

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Evaluating Multi-criteria Evolutionary Algorithms for Airfoil Optimisation

Cited by 10 publications

References 5 publications

Advanced concepts for multi-objective evolutionary optimization in aircraft industry

Advanced concepts for multi-objective evolutionary optimization in aircraft industry

Multiobjective Evolutionary Algorithms in Aeronautical and Aerospace Engineering

A Suite of Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics

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