PCA-Enhanced Metamodel-Assisted Evolutionary Algorithms for Aerodynamic Optimization

Vision is the science that informs us about the biological and evolutionary algorithms that our eyes, opticnerves and brains have chosen over time to see. This article is an attempt to solve the problem of colour to grey conversion, by borrowing ideas from vision science. We introduce an algorithm that measures contrast along the opponent colour directions and use the results to combine a three dimensional colour space into a grey. The results indicate that the proposed algorithm competes with the state of art algorithms.

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PVT-Biomass-Battery systems optimized with evolutionary algorithms for small and highly shaded environments

Renewable Energy and Power Quality Journal ◽

10.24084/repqj14.395 ◽

2016 ◽

pp. 568-573

Author(s):

J.A. Fernández Fernández ◽

P. González-Rodelas ◽

E. Alameda-Hernández

Keyword(s):

Evolutionary Algorithms ◽

Battery Systems

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Multi-Point Design Optimization of Busemann Based Intakes for Scramjet-Powered Ascent Flight via Surrogate-Assisted Evolutionary Algorithms

AIAA Scitech 2021 Forum ◽

10.2514/6.2021-1960 ◽

2021 ◽

Author(s):

Shuvayan Brahmachary ◽

Hideaki Ogawa

Keyword(s):

Evolutionary Algorithms ◽

Design Optimization

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Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

10.26434/chemrxiv.8025221.v2 ◽

2019 ◽

Author(s):

Anders Andreasen

Keyword(s):

Evolutionary Algorithms ◽

Gas Separation ◽

Oil And Gas ◽

Optimal Solution ◽

Computer Experiment ◽

Latin Hypercube Sampling ◽

Surrogate Models ◽

Inlet Temperature ◽

Separation Plant ◽

Separation Stage

In this article the optimization of a realistic oil and gas separation plant has been studied. Two different fluids are investigated and compared in terms of the optimization potential. Using Design of Computer Experiment (DACE) via Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with a variety of different evolutionary algorithms for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with an RVP < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a single optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator apparently two different, yet equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.<br>

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