Constrained Single-Point Aerodynamic Shape Optimization of the DPW-W1 Wing Through Evolutionary Programming and Support Vector Machines

Andrés-Pérez, Esther; González-Juárez, Daniel; Martin-Burgos, Mario J.; Carro-Calvo, L.

doi:10.1007/978-3-319-89988-6_3

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Researchers from the EA community have been endeavoring to apply these algorithms in dynamic environments for over two decades. One of the first attempts to track the optimum in simple dynamic environments was proposed by Bäck [7] using the capabilities of Evolution Strategies (ES) and Evolutionary Programming (EP), which have extensively been used for reinforcement learning [8], fluid dynamics [9] and credit risk evaluation [10]. Branke and Schmeck recognized the importance of dynamic optimization problems for the EA community in real-world problems.…”

Section: Introductionmentioning

confidence: 99%

Online Adaptive Controller Based on Dynamic Evolution Strategies

Fister

Šafarič

et al. 2018

Applied Sciences

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The majority of non-linear systems nowadays are controlled online using rapid PI-controllers with linear characteristics. Evolutionary algorithms are rarely used, especially for online adaptive control, due to their time complexity. This paper proposes an online adaptive controller based on a dynamic evolution strategy and attempts to overcome this performance problem. The main advantage of the evolution strategies over other gradient machine learning algorithms is that they are insensitive to becoming stuck into local optima. As a result, the proposed controller is capable of responding in real-time (sampling time between 1–5 ms) and was tested on a non-linear, single-degree-of-freedom robotic mechanism. To the extent of our knowledge, this is the first application of evolutionary algorithms in such an online control. In general, the results obtained were better than the results achieved using a traditional PI-controller.

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