Surrogate-based Analysis and Optimization

https://eprints.soton.ac.uk/65935/01/Forr_09.pdf

Recent advances in surrogate-based optimization

Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.

/pdf/evolutionary-optimization-in-uncertain-environments-a-survey-1cgcnwsuec.pdf

Evolutionary optimization in uncertain environments-a survey

Evolutionary algorithms (EAs) have received increasing interests both in the academy and industry. One main difficulty in applying EAs to real-world applications is that EAs usually need a large number of fitness evaluations before a satisfying result can be obtained. However, fitness evaluations are not always straightforward in many real-world applications. Either an explicit fitness function does not exist, or the evaluation of the fitness is computationally very expensive. In both cases, it is necessary to estimate the fitness function by constructing an approximate model. In this paper, a comprehensive survey of the research on fitness approximation in evolutionary computation is presented. Main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed. To conclude, open questions and interesting issues in the field are discussed.

https://epubs.surrey.ac.uk/7610/2/SC2005.pdf

A comprehensive survey of fitness approximation in evolutionary computation

Recent progress in flapping wing aerodynamics and aeroelasticity

Response surface approximation of Pareto optimal front in multi-objective optimization

Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design practices and the need for future research are identified.

/pdf/global-design-optimization-for-aerodynamics-and-rocket-vri9xlsaiv.pdf

Global Design Optimization for Aerodynamics and Rocket Propulsion Components

A systematic approach is presented to approximate the Pareto optimal front (POF) by a response surface approximation. The data for the POF is obtained by multi-objective evolutionary algorithm. Improvements to address drift in the POF are also presented. The approximated POF can help visualize and quantify trade-offs among objectives to select compromise designs. The bounds of this approximate POF are obtained using multiple convex-hulls. The proposed approach is applied to study trade-offs among objectives of a rocket injector design problem where performance and life objectives compete. The POF is approximated using a quintic polynomial. The compromise region quantifies trade-offs among objectives. � 2006 Elsevier B.V. All rights reserved.

Response Surface Approximation of Pareto Optimal Front in Multi-Objective Optimization

A sensitivity analysis is done for turbulent cavitating flows using a pressure -based Navier-Stokes solver coupled with a phase volume fraction transport model and non-equilibrium k-e turbulence closure. Four modeling parameters are adopted for evaluation, namely, Ce1 and Ce2, which directly influences the production and dissipation of turbulence kinetic energy, and Cdest and Cprod, which regulate the evaporation and condensation of the phases. Response surface methodology along with design of experiments is used for the sensitivity studies. The difference between the computational and experimental results is used to judge the model fidelity. Under non-cavitating conditions, the best selections of Ce1 and Ce2, exhibit a linear combination with multiple optima. Using this information, cavitating flows around an axi -symmetric geometry with a hemispherical fore-body and the NACA66(MOD) airfoil are assessed. Analysis of the cavitating model shows that the favorable combinations of Cdest and Cprod, are inversely proportional to each other for the geometries considered. A set of cavitation numbers is selected for each of the geometries to demonstrate the predictive capability of the present modeling approach for attached, turbulent cavitating flows.

Rajkumar Vaidyanathan

Papers

Surrogate-based Analysis and Optimization

Response surface approximation of Pareto optimal front in multi-objective optimization

Global Design Optimization for Aerodynamics and Rocket Propulsion Components

Response Surface Approximation of Pareto Optimal Front in Multi-Objective Optimization

Sensitivity Evaluation of a Transport-Based Turbulent Cavitation Model