Investigation of the traveling thief problem

Wuijts, Rogier Hans; Thierens, Dirk

doi:10.1145/3321707.3321766

Cited by 10 publications

(10 citation statements)

References 19 publications

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“…Some papers explored TTP using őtness landscape analysis. In [34] the authors considered local search operators and investigated the őtness landscape characteristics of some smaller instances of the TTP. The local search operators included 2-opt, Insertion, Bitŕip and Exchange and metaheuristics included multi-start local search, iterated local search and genetic local search.…”

Section: Empirical Algorithm Analysismentioning

confidence: 99%

“…Since it was introduced, the TTP received the attention of researchers from the őelds of evolutionary computation, metaheuristics and operations research, mainly due to the fact that the problem is easy to understand, yet challenging to solve. Several papers proposed heuristic solution methods [7,8,21,30], and fewer ones tried to analyse the problem empirically and theoretically [9,33,34]. These analyses focus on the impact of problem features, with little to no attention to the impact of the dependencies between the components.…”

Section: Introductionmentioning

confidence: 99%

“…Once the offspring is generated, it is combined with the population and the best solutions are kept for further improvement (lines [33][34]. The algorithm stops when the maximum number of evaluations T max is reached, or a nearoptimal solution (solution with a gap to optimal smaller than ϵ) is found (line 35).…”

mentioning

confidence: 99%

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A regression analysis of the impact of routing and packing dependencies on the expected runtime

Yafrani

Martins

Wagner

et al. 2023

Preprint

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Problems with multiple interdependent components offer a better representation of the real-world situations where globally optimal solutions are preferred over optimal solutions for the individual components. One such model is the Travelling Thief Problem (TTP); while is appears popular and while it may offer a better benchmarking alternative to the standard benchmarks, only one form of inter-component dependency is investigated. The goal of this paper is to study the impact of different models of dependency on the fitness landscape using performance prediction models (regression analysis). To conduct the analysis, we consider a generalised model of the TTP, where the dependencies between the two components of the problem are tunable via the problem features. The regression model was able to predict the expected runtime of the algorithm based on the problem features. Furthermore, the results show that the contribution of the item value drop dependency is significantly higher than the velocity change dependency.

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Section: Empirical Algorithm Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A regression analysis of the impact of routing and packing dependencies on the expected runtime

Yafrani

Martins

Wagner

et al. 2023

Preprint

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“…Moreover, dynamic TTP variants have been explored [19,47], as well as various multi-objective formulations [2,10,54,57]. To better understand the effect of operators on a more fundamental level, fitness-landscape analyses [56,58] presented correlations and characteristics that are potentially exploitable.…”

Section: The Traveling Thief Problemmentioning

confidence: 99%

Generating Instances with Performance Differences for More Than Just Two Algorithms

Bossek,

Wagner

2021

Preprint

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In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration.We contribute to this branch of research by proposing fitnessfunctions to evolve instances that show large performance differences for more than just two algorithms simultaneously. As a proofof-principle, we evolve instances of the multi-component Traveling Thief Problem (TTP) for three incomplete TTP-solvers. Our results point out that our strategies are promising, but unsurprisingly their success strongly relies on the algorithms' performance complementarity.

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“…Cooperative methods [6,13,43,15,31,46] iteratively alternate between a search for a cyclic tour and another search of a collection plan, keeping one of the two unchanged while searching for the other until no further improvement. Full-encoding methods [26,42,13,14,45] deals with the entire TTP problem at a time using cyclic tour and collection plan changing operators within the same search framework. Hyper-heuristic methods [1,25,24,16] generate or select low level heuristics as neighbourhood operators for cyclic tours or collection plans and use them in search.…”

Section: Introductionmentioning

confidence: 99%

Solving travelling thief problems using coordination based methods

Namazi,

Newton,

Sanderson

et al. 2023

J Heuristics

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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/CoCo.

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Investigation of the traveling thief problem

Cited by 10 publications

References 19 publications

A regression analysis of the impact of routing and packing dependencies on the expected runtime

A regression analysis of the impact of routing and packing dependencies on the expected runtime

Generating Instances with Performance Differences for More Than Just Two Algorithms

Solving travelling thief problems using coordination based methods

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