Automated Antenna Design with Evolutionary Algorithms

Hornby, Gregory S.; Globus, Al; Linden, D.S.; Lohn, Jason

doi:10.2514/6.2006-7242

Cited by 103 publications

(73 citation statements)

References 7 publications

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“…In many of these cases, similarly to HyPro (see section II), a block diagram approach is used to model the physics of the problem and its structure is then optimized with GP. Examples of this approach can be found for dynamical systems [22], [23], control systems development [24], electronic circuit design [25] and also to various problems in aerospace engineering such as the design of an antenna for the NASA Space Technology 5 spacecraft [26], [27].…”

Section: Configurational Optimizationmentioning

confidence: 99%

Configurational optimizer of Combined Cycle Propulsion using Genetic Programming

Mogavero

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Abstract-In most engineering applications the optimization is employed after the conceptual design is already frozen with the only purpose of perfecting it. Although optimization algorithms capable of optimizing the configuration exist, their application is limited to electronics or control design, while for complex systems the conceptual design is often performed with manual trade off analyses among few options.In hypersonic propulsion for instance, the choice of which flow cycle to use is made mainly by means of experience or in the best case by a trade off between radically different architectures. For Combined Cycle Propulsion (CCP) however many flow cycle possibilities are available and it is very possible that the final design is influenced by the preconceptions of the engineers rather than a pure objective judgement. Therefore a configurational optimization process, not bound to any known configuration, can potentially deliver totally new engine concepts, aiding the creativity of the designer.In this paper the first steps toward a configurational optimization of CCP are shown. This optimization is intended to find the optimal engine design without knowing the engine conceptual design a-priori. The optimization algorithm is based on the Genetic Programming (GP) and it was presented for the first time at the 20 th AIAA International Space Planes and Hypersonic Systems and Technologies Conference. In the present paper the fitness function has been improved with the addition of a constraint like penalty function designed to solve a convergence problem outlined in the previous version.The results presented in this paper demonstrate that the optimizer is able to converge to a reasonable configuration, coherent with the engineering practice. Moreover the improvements proposed in this paper proved to be effective in the mitigation of the previously detected problem, thus demonstrating that the optimization algorithm is robust and that the previous problems were only due to a poorly defined fitness function.

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Section: Configurational Optimizationmentioning

confidence: 99%

Configurational optimizer of Combined Cycle Propulsion using Genetic Programming

Mogavero

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…Therefore, to gain high confidence of applying EAs, we need evidence that EAs will work well in future problems. There have been many successful application cases, e.g., antenna design [17], circuit optimization [25], and scheduling [30]. These cases, however, serve more as intuitions from practice rather than rigorous evidences.…”

Section: Introductionmentioning

confidence: 99%

Switch Analysis for Running Time Analysis of Evolutionary Algorithms

Qian

Zhou

2015

IEEE Trans. Evol. Computat.

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-Evolutionary algorithms are a large family of heuristic optimization algorithms. They are problem independent, and have been applied in various optimization problems. Thus general analysis tools are especially appealing for guiding the analysis of evolutionary algorithms in various situations. This paper develops the switch analysis approach for running time analysis of evolutionary algorithms, revealing their average computational complexity. Unlike previous analysis approaches that analyze an algorithm from scratch, the switch analysis makes use of another well analyzed algorithm and, by contrasting them, can lead to better results. We investigate the power of switch analysis by comparing it with two commonly used analysis approaches, the fitness level method and the drift analysis. We define the reducibility between two analysis approaches for comparing their power. By the reducibility relationship, it is revealed that both the fitness level method and the drift analysis are reducible to the switch analysis, as they are equivalent to specific configurations of the switch analysis. We further show that the switch analysis is not reducible to the fitness level method, and compare it with the drift analysis on a concrete analysis case (the Discrete Linear Problem). The reducibility study might shed some light on the unified view of different running time analysis approaches.

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“…We can name the design of thinned array in 1994 by Randy Haupt [12], wire antennas by Derek Linden in 1997, and Derek Linden with Altshuler in 1998 and 1999 [20], patch antennas by M. Johnson and Rahmat-Samii in 1999, linear and planar array by D. Marcano and F. Duran in 1999 [21]. The NASA also work in Evolutionary Algorithms to design Evolutionary Antenna [22]. …”

Section: Design Of Devices Using Genetics Algorithmsmentioning

confidence: 99%