130 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
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
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
Packing While Traveling: Mixed Integer Programming for a Class of Nonlinear Knapsack Problems
Packing and vehicle routing problems play an important role in the area of
supply chain management. In this paper, we introduce a non-linear knapsack
problem that occurs when packing items along a fixed route and taking into
account travel time. We investigate constrained and unconstrained versions of
the problem and show that both are NP-hard. In order to solve the problems, we
provide a pre-processing scheme as well as exact and approximate mixed integer
programming (MIP) solutions. Our experimental results show the effectiveness of
the MIP solutions and in particular point out that the approximate MIP approach
often leads to near optimal results within far less computation time than the
exact approach
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
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