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.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/11707 |
| Date | 09 1900 |
| Creators | Oliver, John M. |
| Contributors | Savill, Mark A., Kipouros, Timoleon |
| Publisher | Cranfield University |
| Source Sets | CRANFIELD1 |
| Language | English |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
| Rights | © Cranfield University, 2014. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. |
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