Towards the Evolution of Novel Vertical-Axis Wind Turbines

Renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. An artificial neural network is used as a surrogate model to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency, resulting in an important cost reduction. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.Comment: 14 pages, 11 figure

Symbolic approaches and artificial intelligence algorithms for solving multi-objective optimisation problems

Problems that have more than one objective function are of great importance in engineering sciences and many other disciplines. This class of problems are known as multi-objective optimisation problems (or multicriteria). The difficulty here lies in the conflict between the various objective functions. Due to this conflict, one cannot find a single ideal solution which simultaneously satisfies all the objectives. But instead one can find the set of Pareto-optimal solutions (Pareto-optimal set) and consequently the Pareto-optimal front is established. Finding these solutions plays an important role in multi-objective optimisation problems and mathematically the problem is considered to be solved when the Pareto-optimal set, i.e. the set of all compromise solutions is found. The Pareto-optimal set may contain information that can help the designer make a decision and thus arrive at better trade-off solutions. The aim of this research is to develop new multi-objective optimisation symbolic algorithms capable of detecting relationship(s) among decision variables that can be used for constructing the analytical formula of Pareto-optimal front based on the extension of the current optimality conditions. A literature survey of theoretical and evolutionary computation techniques for handling multiple objectives, constraints and variable interaction highlights a lack of techniques to handle variable interaction. This research, therefore, focuses on the development of techniques for detecting the relationships between the decision variables (variable interaction) in the presence of multiple objectives and constraints. It attempts to fill the gap in this research by formally extending the theoretical results (optimality conditions). The research then proposes first-order multi-objective symbolic algorithm or MOSA-I and second-order multi-objective symbolic algorithm or MOSA-II that are capable of detecting the variable interaction. The performance of these algorithms is analysed and compared to a current state-of-the-art optimisation algorithm using popular test problems. The performance of the MOSA-II algorithm is finally validated using three appropriately chosen problems from literature. In this way, this research proposes a fully tested and validated methodology for dealing with multi-objective optimisation problems. In conclusion, this research proposes two new symbolic algorithms that are used for identifying the variable interaction responsible for constructing Pareto-optimal front among objectives in multi-objective optimisation problems. This is completed based on a development and relaxation of the first and second-order optimality conditions of Karush-Kuhn-Tucker.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Single- and multi-objective evolutionary design optimization assisted by gaussian random field metamodels

In this thesis numerical optimization methods for single- and multi-objective design optimization with time-consuming computer experiments are studied in theory and practise. We show that the assistance by metamodeling techniques (or: surrogates) can significantly accelerate evolutionary (multi-objective) optimization algorithms (E(M)OA) in the presence of time consuming evaluations. A further increase of robustness can be achieved by taking confidence information for the imprecise evaluations into account. Gaussian random field metamodels, also referred to as Kriging techniques, can provide such confidence information. The confidence information is used to figure out ‘white spots’ in the functional landscape to be explored. The thesis starts with a detailed discussion of computational aspects related to the Kriging algorithm. Then, algorithms for optimization with single objectives, constraints and multiple objectives are introduced. For the latter, with the S-metric selection EMOA (SMS-EMOA) a new powerful algorithm for Pareto optimization is introduced, which outperforms established techniques on standard benchmarks. The concept of a filter is introduced to couple E(M)OA with metamodeling techniques. Various filter concepts are compared, both by means of deducing their properties theoretically and by experiments on artificial landscapes. For the latter studies we propose new analytical indicators, like the inversion metric and the recall/precision measure. Moreover, sufficient conditions for global convergence in probability are established. Finally the practical benefit of the new techniques is demonstrated by solving several industrial optimization problems, including airfoil optimization, solidification process design, metal forming, and electromagnetic compatibility design and comparing the results to those obtained by standard algorithms

Single- and multi-objective airfoil design using genetic algorithms and artificial intelligence

Transonic airfoil design problems are solved using a Genetic Algorithm (GA) based optimizer. At the desired operating point, the minimum drag and constant lift targets are achieved through either a scalarized objective function, involving an arbitrary weighting factor, or the Pareto technique. For the optimization of an airfoil at two operating points, similar approaches are used. The CPU cost of the optimizer is kept low through Arti-cial Intelligence. A multilayer perceptron is trained using already evaluated individuals and provides good, though approximate, tness predictions. With the regularly trained network, the direct ow solver calls are noticeably reduced