A Genetic Algorithm for Multi-component Optimization Problems: The Case of the Travelling Thief Problem

Vieira, Daniel; Soares, Gustavo Luís; Vasconcelos, J.A.; Mendes, Maria Célia

doi:10.1007/978-3-319-55453-2_2

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…Additionally, the authors investigated a TTP-specific local search algorithm. A Genetic Algorithm was used in [14]. Authors solve the overall problem instead of solving the subproblems separately.…”

Section: Related Workmentioning

confidence: 99%

A Specialized Evolutionary Approach to the bi-objective Travelling Thief Problem

Laszczyk¹,

Myszkowski²

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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In the recent years, it has been shown that real world-problems are often comprised of two, interdependent subproblems. Often, solving them independently does not lead to the solution to the entire problem. In this article, a Travelling Thief Problem is considered, which combines a Travelling Salesman Problem with a Knapsack Problem. A Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is investigated, along with its recent modification-a Non-Dominated Tournament Genetic Algorithm (NTGA). Each method is investigated in two configurations. One, with generic representation, and genetic operators. The other, specialized to the given problem, to show how the specialization of genetic operators leads to better results. The impact of the modifications introduced by NTGA is verified. A set of Quality Measures is used to verify the convergence, and diversity of the resulting PF approximations, and efficiency of the method. A set of experiments is carried out. It is shown that both methods work almost the same when generic representation is used. However, NTGA outperforms classical NSGA-II in the specialized results.

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Section: Related Workmentioning

confidence: 99%

A Specialized Evolutionary Approach to the bi-objective Travelling Thief Problem

Laszczyk¹,

Myszkowski²

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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“…Real-world problems, like supply chains, are characterised by interactions, where subproblems features are linked [2]. Hence, an optimal solution for each subproblem might not guarantee optimal overall solutions [12].…”

Section: Introductionmentioning

confidence: 99%

Facility location problem and permutation flow shop scheduling problem

Ogunsemi

McCall

Kern

et al. 2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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There is a growing literature spanning several research communities that studies multiple optimisation problems whose solutions interact, thereby leading researchers to consider suitable approaches to joint solution. Real-world problems, like supply chain, are systems characterised by such interactions. A decision made at one point of the supply chain could have significant consequence that ripples through linked production and transportation systems. Such interactions would require complex algorithmic designs. This paper, therefore, investigates the linkages between a facility location and permutation flow shop scheduling problems of a distributed manufacturing system with identical factory (FLPPFSP). We formulate a novel mathematical model from a linked optimisation perspective with objectives of minimising facility cost and makespan. We present three algorithmic approaches in tackling FLPPFSP; Nondominated Sorting Genetic Algorithm for Linked Problem (NS-GALP), Multi-Criteria Ranking Genetic Algorithm for Linked Problem (MCRGALP), and Sequential approach. To understand FLPPFSP linkages, we conduct a pre-assessment by randomly generating 10000 solution pairs on all combined problem instances and compute their respective correlation coefficients. Finally, we conduct experiments to compare results obtained by the selected algorithmic methods on 620 combined problem instances. Empirical results demonstrate that NSGALP outperforms the other two methods based on relative hypervolume, hypervolume and epsilon metrics. CCS CONCEPTS• Applied computing → Supply chain management.

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“…However, adopting heuristics to solve TTP can be computationally complex, especially with a large number of instances [7]. Evolutionary approaches such as genetic algorithms (GAs) and genetic programming have also been adopted for roughly solving the TTP, as in [6,[8][9][10][11][12][13][14]. The majority of these approaches try to improve TTP solutions by considering each subproblem (i.e., TSP and KP) independently, despite the interdependence between the subcomponents.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Algorithm with Tabu Search for Optimization of the Traveling Thief Problem

Alharbi¹

2018

ijacsa

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Until now, several approaches such as evolutionary computing and heuristic methods have been presented to optimize the traveling thief problem (TTP). However, most of these approaches consider the TTP components independently, usually considering the traveling salesman problem (TSP) and then tackling the knapsack problem (KP), despite their interdependent nature. In this paper, we investigate the use of a hybrid genetic algorithm (GA) and tabu search (TS) for the TTP. Therefore, a novel hybrid genetic approach called GATS is proposed and compared with the state-of-the-art approaches. The key aspect of GATS is that TTP solutions are considered by firmly taking into account the interdependent nature of the TTP subcomponents, where all its operators are simultaneously implemented on TSP and KP solutions. A comprehensive set of TTP benchmark datasets was adopted to investigate the effectiveness of GATS. We selected 540 instances for our investigation, which comprised five different groups of cities (51, 52, 76, 100 and 150 cities) and different groupings of items, from 50 to 745 items. All types of knapsack (uncorrelated, uncorrelated with similar weights and bonded strongly correlated) with all different knapsack capacities were also taken into consideration. Different initialization methods were empirically investigated as well. The results of the computational experiments demonstrated that GATS is capable of surpassing the state-of-the-art results for various instances.

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A Genetic Algorithm for Multi-component Optimization Problems: The Case of the Travelling Thief Problem

Cited by 8 publications

References 9 publications

A Specialized Evolutionary Approach to the bi-objective Travelling Thief Problem

A Specialized Evolutionary Approach to the bi-objective Travelling Thief Problem

Facility location problem and permutation flow shop scheduling problem

A Hybrid Genetic Algorithm with Tabu Search for Optimization of the Traveling Thief Problem

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