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Paper 2011.05081 paper by Community
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Paper 2011.05081

by Community ID: arxiv-paper--2011.05081

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 solution for the other components. This can be a challenge for ...

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Registry ID arxiv-paper--2011.05081
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BibTeX 
@misc{arxiv_paper__2011.05081,
  author = {Community},
  title = {Paper 2011.05081 Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2011.05081v2}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Community. (2026). Paper 2011.05081 [Paper]. Free2AITools. https://arxiv.org/abs/2011.05081v2

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The Nexus Index for Paper 2011.05081 aggregates Popularity (P:0), Velocity (V:0), and Credibility (C:0). The Utility score (U:0) represents deployment readiness, context efficiency, and structural reliability within the Nexus ecosystem.

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πŸ“ Executive Summary

"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 solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective form..."

❝ Cite Node 

@article{Chagas2020ArXiv,
  title={ArXiv 2011.05081 Technical Profile},
  author={Jonatas B. C. Chagas and Markus Wagner},
  journal={arXiv preprint arXiv:arxiv-paper--2011.05081},
  year={2020}
}

πŸ‘₯ Collaborating Minds

Jonatas B. C. Chagas Markus Wagner

Abstract & Analysis

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 solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances.

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id
arxiv-paper--2011.05081
author
Community
tags
arxiv:cs.NE

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