28 research outputs found
Surrogate Assisted Optimisation for Travelling Thief Problems
The travelling thief problem (TTP) is a multi-component optimisation problem
involving two interdependent NP-hard components: the travelling salesman
problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP
solvers modify the underlying TSP and KP solutions in an iterative and
interleaved fashion. The TSP solution (cyclic tour) is typically changed in a
deterministic way, while changes to the KP solution typically involve a random
search, effectively resulting in a quasi-meandering exploration of the TTP
solution space. Once a plateau is reached, the iterative search of the TTP
solution space is restarted by using a new initial TSP tour. We propose to make
the search more efficient through an adaptive surrogate model (based on a
customised form of Support Vector Regression) that learns the characteristics
of initial TSP tours that lead to good TTP solutions. The model is used to
filter out non-promising initial TSP tours, in effect reducing the amount of
time spent to find a good TTP solution. Experiments on a broad range of
benchmark TTP instances indicate that the proposed approach filters out a
considerable number of non-promising initial tours, at the cost of omitting
only a small number of the best TTP solutions
Exact and heuristic approaches for multi-component optimisation problems
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
Solving Travelling Thief Problems using Coordination Based Methods
A travelling thief problem (TTP) is a proxy to real-life problems such as
postal collection. TTP comprises an entanglement of a travelling salesman
problem (TSP) and a knapsack problem (KP) since items of KP are scattered over
cities of TSP, and a thief has to visit cities to collect items. In TTP, city
selection and item selection decisions need close coordination since the
thief's travelling speed depends on the knapsack's weight and the order of
visiting cities affects the order of item collection. Existing TTP solvers deal
with city selection and item selection separately, keeping decisions for one
type unchanged while dealing with the other type. This separation essentially
means very poor coordination between two types of decision. In this paper, we
first show that a simple local search based coordination approach does not work
in TTP. Then, to address the aforementioned problems, we propose a human
designed coordination heuristic that makes changes to collection plans during
exploration of cyclic tours. We further propose another human designed
coordination heuristic that explicitly exploits the cyclic tours in item
selections during collection plan exploration. Lastly, we propose a machine
learning based coordination heuristic that captures characteristics of the two
human designed coordination heuristics. Our proposed coordination based
approaches help our TTP solver significantly outperform existing
state-of-the-art TTP solvers on a set of benchmark problems. Our solver is
named Cooperation Coordination (CoCo) and its source code is available from
https://github.com/majid75/CoCoComment: expanded and revised version of arXiv:1911.0312
A comparative study of evolutionary approaches to the bi-objective dynamic Travelling Thief Problem
Dynamic evolutionary multi-objective optimization is a thriving research area. Recent contributions span the development of specialized algorithms and the construction of challenging benchmark problems. Here, we continue these research directions through the development and analysis of a new bi-objective problem, the dynamic Travelling Thief Problem (TTP), including three modes of dynamic change: city locations, item profit values, and item availability. The interconnected problem components embedded in the dynamic problem dictate that the effective tracking of good trade-off solutions that satisfy both objectives throughout dynamic events is non-trivial. Consequently, we examine the relative contribution to the non-dominated set from a variety of population seeding strategies, including exact solvers and greedy algorithms for the knapsack and tour components, and random techniques. We introduce this responsive seeding extension within an evolutionary algorithm framework. The efficacy of alternative seeding mechanisms is evaluated across a range of exemplary problem instances using ranking-based and quantitative statistical comparisons, which combines performance measurements taken throughout the optimization. Our detailed experiments show that the different dynamic TTP instances present varying difficulty to the seeding methods tested. We posit the dynamic TTP as a suitable benchmark capable of generating problem instances with different controllable characteristics aligning with many real-world problems
Evolutionary Diversity Optimisation for The Traveling Thief Problem
There has been a growing interest in the evolutionary computation community
to compute a diverse set of high-quality solutions for a given optimisation
problem. This can provide the practitioners with invaluable information about
the solution space and robustness against imperfect modelling and minor
problems' changes. It also enables the decision-makers to involve their
interests and choose between various solutions. In this study, we investigate
for the first time a prominent multi-component optimisation problem, namely the
Traveling Thief Problem (TTP), in the context of evolutionary diversity
optimisation. We introduce a bi-level evolutionary algorithm to maximise the
structural diversity of the set of solutions. Moreover, we examine the
inter-dependency among the components of the problem in terms of structural
diversity and empirically determine the best method to obtain diversity. We
also conduct a comprehensive experimental investigation to examine the
introduced algorithm and compare the results to another recently introduced
framework based on the use of Quality Diversity (QD). Our experimental results
show a significant improvement of the QD approach in terms of structural
diversity for most TTP benchmark instances.Comment: To appear at GECCO 202
Evolutionary Diversity Optimisation for Combinatorial Problems
Diversity optimisation explores a variety of solutions for the intended problem and is rapidly growing and getting more popular within the evolutionary computation community as a result. There can be found several studies that introduce and examine evolutionary approaches to compute a diverse set of solutions for optimisation problems in the continuous domain. To the best of our knowledge, the discrete problems are yet to be studied in the context of diversity optimisation. Thus, this thesis focuses on combinatorial optimisation problems with discrete solution spaces. Here, we compute and explore such solution sets for several noticeable combinatorial problems. We aim to introduce and design evolutionary algorithms capable of computing a diverse set of solutions for the given combinatorial optimisation problem. First, we begin with a comprehensive literature review of the recent developments and then dig deep into two prominent diverse paradigms in evolutionary computation: evolutionary diversity optimisation and quality diversity. These concepts have gained a considerable amount of attention in recent years. Quality diversity aims to achieve diversity in behavioural spaces, while evolutionary diversity optimisation sees diversity in the structural properties of solutions. We study the evolutionary algorithms for the travelling salesperson problem, the travelling thief program, the knapsack problem, and finally, the Boolean satisfiability problem. The prospective results demonstrate the capability of the introduced algorithms to achieve diverse and high-quality solutions.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202