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The Traveling Thief Problem (TTP) is a multi-component optimization problem that captures the interplay between routing and packing decisions by combining the classical Traveling Salesperson Problem (TSP) and the Knapsack Problem (KP). The…

Data Structures and Algorithms · Computer Science 2026-04-22 Jan Eube , Kelin Luo , Aneta Neumann , Frank Neumann , Heiko Röglin

Many real-world optimization problems have multiple interacting components. Each of these can be NP-hard and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal…

Neural and Evolutionary Computing · Computer Science 2021-09-13 Jonatas B. C. Chagas , Markus Wagner

Understanding the interactions between different combinatorial optimisation problems in real-world applications is a challenging task. Recently, the traveling thief problem (TTP), as a combination of the classical traveling salesperson…

Data Structures and Algorithms · Computer Science 2017-02-20 Frank Neumann , Sergey Polyakovskiy , Martin Skutella , Leen Stougie , Junhua Wu

The Travelling Thief Problem (TTP) is a challenging combinatorial optimization problem that attracts many scholars. The TTP interconnects two well-known NP-hard problems: the Travelling Salesman Problem (TSP) and the 0-1 Knapsack Problem…

Artificial Intelligence · Computer Science 2020-12-17 Lei Yang , Zitong Zhang , Xiaotian Jia , Peipei Kang , Wensheng Zhang , Dongya Wang

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…

Neural and Evolutionary Computing · Computer Science 2022-04-07 Adel Nikfarjam , Aneta Neumann , Frank Neumann

Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in…

Neural and Evolutionary Computing · Computer Science 2022-07-29 Adel Nikfarjam , Aneta Neumann , Jakob Bossek , Frank Neumann

In real-world optimisation, it is common to face several sub-problems interacting and forming the main problem. There is an inter-dependency between the sub-problems, making it impossible to solve such a problem by focusing on only one…

Neural and Evolutionary Computing · Computer Science 2023-01-05 Adel Nikfarjam , Aneta Neumann , Frank Neumann

Many real-world optimisation problems involve dynamic and stochastic components. While problems with multiple interacting components are omnipresent in inherently dynamic domains like supply-chain optimisation and logistics, most research…

Neural and Evolutionary Computing · Computer Science 2020-09-16 Ragav Sachdeva , Frank Neumann , Markus Wagner

In this paper, we propose a method to solve a bi-objective variant of the well-studied Traveling Thief Problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: Traveling Salesman Problem (TSP)…

Neural and Evolutionary Computing · Computer Science 2020-07-29 Jonatas B. C. Chagas , Julian Blank , Markus Wagner , Marcone J. F. Souza , Kalyanmoy Deb

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…

Artificial Intelligence · Computer Science 2016-03-25 Mohamed El Yafrani , Belaïd Ahiod

Many evolutionary and constructive heuristic approaches have been introduced in order to solve the Traveling Thief Problem (TTP). However, the accuracy of such approaches is unknown due to their inability to find global optima. In this…

Data Structures and Algorithms · Computer Science 2017-08-02 Junhua Wu , Markus Wagner , Sergey Polyakovskiy , Frank Neumann

Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments. It is important to understand the behaviour of evolutionary algorithms for complex optimization problems…

Neural and Evolutionary Computing · Computer Science 2023-05-31 Jakob Bossek , Aneta Neumann , Frank Neumann

The chance-constrained knapsack problem is a variant of the classical knapsack problem where each item has a weight distribution instead of a deterministic weight. The objective is to maximize the total profit of the selected items under…

Neural and Evolutionary Computing · Computer Science 2020-04-09 Yue Xie , Aneta Neumann , Frank Neumann

The travelling thief problem (TTP) is a well-known multi-component optimisation problem that captures the interdependence between two components: the tour across cities and the packing of items. The packing while travelling problem (PWT) is…

Neural and Evolutionary Computing · Computer Science 2026-04-16 Thilina Pathirage Don , Aneta Neumann , Frank Neumann

The Traveling Thief Problem is an NP-hard combination of the well known traveling salesman and knapsack packing problems. In this paper, we use symbolic regression to learn useful features of near-optimal packing plans, which we then use to…

Neural and Evolutionary Computing · Computer Science 2024-04-22 Andrew Ni , Lee Spector

Investigation of detailed and complex optimisation problem formulations that reflect realistic scenarios is a burgeoning field of research. A growing body of work exists for the Travelling Thief Problem, including multi-objective…

Neural and Evolutionary Computing · Computer Science 2020-02-10 Daniel Herring , Michael Kirley , Xin Yao

We tackle the Thief Orienteering Problem (ThOP), an academic multi-component problem that combines two classical combinatorial problems, namely the Knapsack Problem and the Orienteering Problem. In the ThOP, a thief has a time limit to…

Neural and Evolutionary Computing · Computer Science 2021-10-28 Jonatas B. C. Chagas , Markus Wagner

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…

Artificial Intelligence · Computer Science 2023-03-01 Majid Namazi , Conrad Sanderson , M. A. Hakim Newton , Abdul Sattar

The performance of base-line Evolutionary Algorithms (EAs) on combinatorial problems has been studied rigorously. From the theoretical viewpoint, the literature extensively investigates the linear problems, while the theoretical analysis of…

Neural and Evolutionary Computing · Computer Science 2019-07-02 Vahid Roostapour , Mojgan Pourhassan , Frank Neumann

Evolutionary algorithms are particularly effective for optimisation problems with dynamic and stochastic components. We propose multi-objective evolutionary approaches for the knapsack problem with stochastic profits under static and…

Neural and Evolutionary Computing · Computer Science 2024-04-15 Kokila Kasuni Perera , Aneta Neumann
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