Solving the Bi-objective Traveling Thief Problem with Multi-objective Evolutionary Algorithms

Blank, Julian; Deb, Kalyanmoy; Mostaghim, Sanaz

doi:10.1007/978-3-319-54157-0_4

Cited by 28 publications

(25 citation statements)

References 12 publications

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“…Additionally, novel indicators were proposed, and the approach was compared to state-of-the-art methods. In [4], an NSGA-II with specialized representation and genetic operators was investigated. Various crossover and mutations method were investigated and the best configuration was identified.…”

Section: Related Workmentioning

confidence: 99%

“…In this article, two representations are used. A generic one, presented in [10], and specialized for TTP, presented in [4]. 1) Generic: The genome comprises two parts.…”

Section: B Representationmentioning

confidence: 99%

“…Recently, authors of paper [4] have pointed out, that realworld problems comprise of multiple subproblems. They contain many dependencies and interwovenness.…”

Section: Introductionmentioning

confidence: 99%

“…Due to its interdependence and multi-objective nature, evolutionary approaches show great potential in solving the TTP [6], [7], [8]. In [4] it has been shown that, in case of TTP, classical Non-Dominated Sorting Genetic Algorithm (NSGA-II) [9] with specialized operators outperforms other methods. Authors of [10] have introduced a Non-Dominated Tournament Genetic Algorithm.…”

Section: Introductionmentioning

confidence: 99%

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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%

“…In this article, two representations are used. A generic one, presented in [10], and specialized for TTP, presented in [4]. 1) Generic: The genome comprises two parts.…”

Section: B Representationmentioning

confidence: 99%

“…Recently, authors of paper [4] have pointed out, that realworld problems comprise of multiple subproblems. They contain many dependencies and interwovenness.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…The heuristic approaches or operators that take advantage of the existing exact algorithms of the TTP [20,30] are yet lacking. On the other hand, very few investigations have been taken on the approaches of the multi-objective formulations of the TTP except by Blank, Deb, and Mostaghim [3], Yafrani, Chand, Neumann, Ahiod, and Wagner [32].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary computation plus dynamic programming for the bi-objective travelling thief problem

Polyakovskiy

Wagner

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference

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This research proposes a novel indicator-based hybrid evolutionary approach that combines approximate and exact algorithms. We apply it to a new bi-criteria formulation of the travelling thief problem, which is known to the Evolutionary Computation community as a benchmark multi-component optimisation problem that interconnects two classical N P-hard problems: the travelling salesman problem and the 0-1 knapsack problem. Our approach employs the exact dynamic programming algorithm for the underlying Packing-While-Travelling problem as a subroutine within a bi-objective evolutionary algorithm. This design takes advantage of the data extracted from Pareto fronts generated by the dynamic program to achieve better solutions. Furthermore, we develop a number of novel indicators and selection mechanisms to strengthen synergy of the two algorithmic components of our approach. The results of computational experiments show that the approach is capable to outperform the state-of-the-art results for the single-objective case of the problem.

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Convergence Behaviour of Population Size and Mutation Rate for NSGA-II in the Context of the Traveling Thief Problem

Garbaruk

Logofătu

2020

Computational Collective Intelligence

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Solving the Bi-objective Traveling Thief Problem with Multi-objective Evolutionary Algorithms

Cited by 28 publications

References 12 publications

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

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

Evolutionary computation plus dynamic programming for the bi-objective travelling thief problem

Convergence Behaviour of Population Size and Mutation Rate for NSGA-II in the Context of the Traveling Thief Problem

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