Modern real world applications are commonly complex, consisting of multiple subsystems
that may interact with or depend on each other. Our case-study about wave
energy converters (WEC) for the renewable energy industry shows that in such a
multi-component system, optimising each individual component cannot yield global
optimality for the entire system, owing to the influence of their interactions or the
dependence on one another. Moreover, modelling a multi-component problem is
rarely easy due to the complexity of the issues, which leads to a desire for existent
models on which to base, and against which to test, calculations. Recently,
the travelling thief problem (TTP) has attracted significant attention in the Evolutionary
Computation community. It is intended to offer a better model for multicomponent
systems, where researchers can push forward their understanding of
the optimisation of such systems, especially for understanding of the interconnections
between the components. The TTP interconnects with two classic NP-hard
problems, namely the travelling salesman problem and the 0-1 knapsack problem,
via the transportation cost that non-linearly depends on the accumulated weight
of items. This non-linear setting introduces additional complexity. We study this
nonlinearity through a simplified version of the TTP - the packing while travelling
(PWT) problem, which aims to maximise the total reward for a given travelling tour.
Our theoretical and experimental investigations demonstrate that the difficulty of a
given problem instance is significantly influenced by adjusting a single parameter,
the renting rate, which prompted our method of creating relatively hard instances
using simple evolutionary algorithms. Our further investigations into the PWT
problem yield a dynamic programming (DP) approach that can solve the problem in
pseudo polynomial time and a corresponding approximation scheme. The experimental
investigations show that the new approaches outperform the state-of-the-art
ones. We furthermore propose three exact algorithms for the TTP, based on the DP
of the PWT problem. By employing the exact DP for the underlying PWT problem
as a subroutine, we create a novel indicator-based hybrid evolutionary approach for
a new bi-criteria formulation of the TTP. This hybrid design takes advantage of the
DP approach, along with a number of novel indicators and selection mechanisms
to achieve better solutions. The results of computational experiments show that the
approach is capable to outperform the state-of-the-art results.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201
