Solving complex real-world problems often involves the simultaneous optimisation
of multiple con
icting performance criteria, these real-world problems
occur in the elds of engineering, economics, chemistry, manufacturing, physics
and many more. The optimisation process usually involves some design challenges
in the form of the optimisation of a number of objectives and constraints. There
exist many traditional optimisation methods (calculus based, random search,
enumerative, etc...), however, these only o er a single solution in either adequate
performance in a narrow problem domain or inadequate performance across a
broad problem domain.
Evolutionary Multi-objective Optimisation (EMO) algorithms are robust optimisers
which are suitable for solving complex real-world multi-objective optimisation
problems, as they are able to address each of the con
icting objectives
simultaneously. Typically, these EMO algorithms are run non-interactively with
a Decision Maker (DM) setting the initial parameters of the algorithm and then
analysing the results at the end of the optimisation process. When EMO is
applied to real-world optimisation problems there is often a DM who is only interested
in a portion of the Pareto-optimal front, however, incorporation of DM
preferences is often neglected in the EMO literature.
In this thesis, the incorporation of DM preferences into EMO search methods
has been explored. This has been achieved through the review of EMO literature
to identify a powerful method of variation, Covariance Matrix Adaptation
(CMA), and its computationally infeasible EMO implementation, MO-CMA-ES.
A CMA driven EMO algorithm, CMA-PAES, capable of optimisation in the
presence of many objectives has been developed, benchmarked, and statistically
veri ed to outperform MO-CMA-ES and MOEA/D-DRA on selected test suites.
CMA-PAES and MOEA/D-DRA with the incorporation of the novel Weighted
Z-score (WZ) preference articulation operator (supporting a priori, a posteriori
or progressive incorporation) are then benchmarked on a range of synthetic and
real-world problems. WZ-CMA-PAES is then successfully applied to a real-world
problem regarding the optimisation of a classi er for concealed weapon detection,
outperforming previously published classi er implementations