Robust evolutionary algorithms for UAV/UCAV aerodynamic and RCS design optimisation

Lee, Dong-Seop; Gonzalez, Felipé; Srinivas, K.; Périaux, Jacques

doi:10.1016/j.compfluid.2007.07.008

Cited by 38 publications

(18 citation statements)

References 8 publications

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“…Lee et al [49,50] made use of a generic framework for multidisciplinary design and optimization [31] to explore the application of a robust MOEA-based algorithm for improving the aerodynamic and radar cross section characteristics of an UCAV (Unmanned Combat Aerial Vehicle). In both applications, two disciplines are considered, the first concerning the aerodynamic efficiency and the second one dealing with the visual and radar signature of an UCAV airplane.…”

Section: Use Of Problem Approximationmentioning

confidence: 99%

A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization

Santana‐Quintero

Montano

Coello

2010

Evolutionary Learning and Optimization

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Abstract. Evolutionary algorithms have been very popular for solving multiobjective optimization problems, mainly because of their ease of use, and their wide applicability. However, multi-objective evolutionary algorithms (MOEAs) tend to consume an important number of objective function evaluations, in order to achieve a reasonably good approximation of the Pareto front. This is a major concern when attempting to use MOEAs for real-world applications, since we can normally afford only a fairly limited number of fitness function evaluations in such cases. Despite these concerns, relatively few efforts have been reported in the literature to reduce the computational cost of MOEAs. It has been until relatively recently, that researchers have developed techniques to achieve an effective reduction of fitness function evaluations by exploiting knowledge acquired during the search. In this chapter, we analyze different proposals currently available in the specialized literature to deal with expensive functions in evolutionary multi-objective optimization. Additionally, we review some real-world applications of these methods, which can be seen as case studies in which such techniques led to a substantial reduction in the computational cost of the MOEA adopted. Finally, we also indicate some of the potential paths for future research in this area.

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Section: Use Of Problem Approximationmentioning

confidence: 99%

A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization

Santana‐Quintero

Montano

Coello

2010

Evolutionary Learning and Optimization

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“…Hence, for this case study the Physical Optics (PO) tool GRECO (Rius Casals et al, 1993) is used as a basis for all the simulations to better capture the physics of the problem, and then it is integrated into the conceptual design framework by using the undeniably faster, but yet less accurate, alternative of metamodels. To further decrease the computational time, a common simplification is to reduce the "threat" angular sectors to either one (Mäkinen et al, 1999) or two (Tianyuan and Xiongqing, 2009), while a technique that has also shown promising results is to simulate a small number of points and then make predictions based on interpolation (Lee et al, 2008). For reasons of simplicity and speed, the approach of analyzing one sector is followed in the first and elementary case study that is presented in paper III, whereas the more intricate strategy of multiple simulations is followed in the second and more advanced case study of paper IV (see Figure 21).…”

Section: Basic and Case-specific Modelsmentioning

confidence: 99%

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Παπαγεωργίου¹

2018

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Over the last decade, Unmanned Aerial Vehicles (UAVs) have experienced an accelerated growth, and nowadays they are being deployed in a variety of missions that have traditionally been covered by manned aircraft. This unprecedented market expansion has created new and unforeseen challenges for the manufacturing industry which is now called to further reduce the idea-to-market times while simultaneously delivering designs of even higher performance. In this environment of uncertainty and risk, it is without a doubt crucial for the involved actors to find ways to secure their strategic advantage, and hence, implementing the latest design tools has become a critical consideration in every Product Development Process (PDP).To this end, a method that has been frequently applied in the PDP and has shown many successful results in the development of complex engineering products is Multidisciplinary Design Optimization (MDO). In general, MDO can bring additional knowledge regarding the best-suited designs much earlier in the process, and in this respect, it can lead to significant cost and time savings by reducing the total number of refinement iterations. Nevertheless, the organizational and cultural integration of MDO has been often overlooked, while at the same time, several technical aspects of the method for UAV design are still at an elementary level. On the whole, research on MDO is showing a slow progress, and to this date, there are many limitations in both the disciplinary models and the available analysis capabilities.In light of the above, this thesis focuses on the particulars of the MDO methodology, and more specifically, on how it can be best adapted and evolved in order to enhance the development process of UAVs. The primary objective is to study the current trends and gaps of the MDO practices in UAV applications, and subsequently to build upon that and explore how these can be included in a roadmap that will be able to serve a guide for newcomers in the field. Compared to other studies, the problem is herein approached from both a technical as well as organizational perspective, and thus, this research not only aims to propose techniques that can lead to better designs but also solutions that will be meaningful to the PDP. Having established the above foundation, this work shows that the traditional MDO frameworks for UAV design have been neglecting several important features, and it elaborates on how those novel elements can be modeled in order to enable a better integration of MDO into the organizational functions. Overall, this thesis presents quantitative and qualitative data which illustrate the effectiveness of the new framework enhancements in the development process of UAVs, and concludes with discussions on the possible improvement directions towards achieving more and better MDO capabilities. vi vii

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“…Jin and Branke [24] made a thorough survey of applying evolutionary computation in uncertain environments. Most recently, Lee et al [25] use robust evolutionary algorithms for unmanned (combat) aerial vehicle aerodynamics and radio cross section design optimization. One advantage of using genetic algorithms is its convenience to solve the optimization problem with both discrete and continuous design variables.…”

Section: Modeling Uncertainty In Mems Fabrication Processmentioning

confidence: 99%

Improved Differential Evolution Based on Stochastic Ranking for Robust Layout Synthesis of MEMS Components

Fan

Liu

Sørensen

et al. 2009

IEEE Trans. Ind. Electron.

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Abstract-This paper introduces an improved differential evolution (DE) algorithm for robust layout synthesis of microelectromechanical system components subject to inherent geometric uncertainties. A case study of the layout synthesis of a combdriven microresonator shows that the approach proposed in this paper can lead to design results that meet the target performance and are less sensitive to geometric uncertainties than the typical designs. It is also demonstrated that the algorithm proposed in this paper cannot only obtain better results than the standard DE algorithm but also outperform some other state-of-the-art algorithms in constrained optimization. Index Terms-Differential evolution (DE), microelectromechanical systems (MEMS), robust design, stochastic ranking (SR).

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Robust evolutionary algorithms for UAV/UCAV aerodynamic and RCS design optimisation

Cited by 38 publications

References 8 publications

A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization

A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Improved Differential Evolution Based on Stochastic Ranking for Robust Layout Synthesis of MEMS Components

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