11,073 research outputs found
Cosolver2B: An Efficient Local Search Heuristic for the Travelling Thief Problem
Real-world problems are very difficult to optimize. However, many researchers
have been solving benchmark problems that have been extensively investigated
for the last decades even if they have very few direct applications. The
Traveling Thief Problem (TTP) is a NP-hard optimization problem that aims to
provide a more realistic model. TTP targets particularly routing problem under
packing/loading constraints which can be found in supply chain management and
transportation. In this paper, TTP is presented and formulated mathematically.
A combined local search algorithm is proposed and compared with Random Local
Search (RLS) and Evolutionary Algorithm (EA). The obtained results are quite
promising since new better solutions were found.Comment: 12th ACS/IEEE International Conference on Computer Systems and
Applications (AICCSA) 2015. November 17-20, 201
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
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
On the Fitness Landscapes of Interdependency Models in the Travelling Thief Problem
Since its inception in 2013, the Travelling Thief Problem (TTP) has been
widely studied as an example of problems with multiple interconnected
sub-problems. The dependency in this model arises when tying the travelling
time of the "thief" to the weight of the knapsack. However, other forms of
dependency as well as combinations of dependencies should be considered for
investigation, as they are often found in complex real-world problems. Our goal
is to study the impact of different forms of dependency in the TTP using a
simple local search algorithm. To achieve this, we use Local Optima Networks, a
technique for analysing the fitness landscape
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