Multi-objective optimisation methods applied to complex engineering systems
| dc.contributor.advisor | Savill, Mark A. | |
| dc.contributor.advisor | Kipouros, Timoleon | |
| dc.contributor.author | Oliver, John M. | |
| dc.date.accessioned | 2017-04-05T08:44:09Z | |
| dc.date.available | 2017-04-05T08:44:09Z | |
| dc.date.issued | 2014-09 | |
| dc.description.abstract | This research proposes, implements and analyses a novel framework for multiobjective optimisation through evolutionary computing aimed at, but not restricted to, real-world problems in the engineering design domain. Evolutionary algorithms have been used to tackle a variety of non-linear multiobjective optimisation problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the number of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimising evolutionary algorithm framework, incorporating a genetic algorithm, that uses self-adaptive mutation and crossover in an attempt to avoid such problems, and which has been benchmarked against both standard optimisation test problems in the literature and a real-world airfoil optimisation case. For this last case, the minimisation of drag and maximisation of lift coefficients of a well documented standard airfoil, the framework is integrated with a freeform deformation tool to manage the changes to the section geometry, and XFoil, a tool which evaluates the airfoil in terms of its aerodynamic efficiency. The performance of the framework on this problem is compared with those of two other heuristic MOO algorithms known to perform well, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this framework achieves better or at least no worse convergence. The framework of this research is then considered as a candidate for smart (electricity) grid optimisation. Power networks can be improved in both technical and economical terms by the inclusion of distributed generation which may include renewable energy sources. The essential problem in national power networks is that of power flow and in particular, optimal power flow calculations of alternating (or possibly, direct) current. The aims of this work are to propose and investigate a method to assist in the determination of the composition of optimal or high-performing power networks in terms of the type, number and location of the distributed generators, and to analyse the multi-dimensional results of the evolutionary computation component in order to reveal relationships between the network design vector elements and to identify possible further methods of improving models in future work. The results indicate that the method used is a feasible one for the achievement of these goals, and also for determining optimal flow capacities of transmission lines connecting the bus bars in the network. | en_UK |
| dc.identifier.uri | http://dspace.lib.cranfield.ac.uk/handle/1826/11707 | |
| dc.language.iso | en | en_UK |
| dc.publisher | Cranfield University | en_UK |
| dc.rights | © Cranfield University, 2014. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. | en_UK |
| dc.subject | Evolutionary | en_UK |
| dc.subject | Algorithm | en_UK |
| dc.subject | Self-Adaptive | en_UK |
| dc.subject | Framework | en_UK |
| dc.subject | Electrical Power | en_UK |
| dc.subject | Plexos | en_UK |
| dc.subject | Power Flow | en_UK |
| dc.subject | Network | en_UK |
| dc.subject | Grid | en_UK |
| dc.subject | MOOEA | en_UK |
| dc.subject | Multi-Objective | en_UK |
| dc.subject | Optimization | en_UK |
| dc.subject | MOO | en_UK |
| dc.subject | MOOP | en_UK |
| dc.subject | Airfoil | en_UK |
| dc.title | Multi-objective optimisation methods applied to complex engineering systems | en_UK |
| dc.type | Thesis or dissertation | en_UK |
| dc.type.qualificationlevel | Doctoral | en_UK |
| dc.type.qualificationname | PhD | en_UK |