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Authors: Anton Duca 1 and Ibrahim Hameed 2

Affiliations: 1 Politehnica University of Bucharest, Faculty of Electrical Engineering, Bucharest, Romania ; 2 NTNU University, Faculty of Information Technology and Electrical Engineering, Alesund, Norway

ISBN: 978-989-758-475-6

ISSN: 2184-2825

Keyword(s): Ant Algorithms, Optimization, Non-Destructive Electromagnetic Testing (NDET), Inverse Problems.

Abstract: The paper proposes and studies the efficiency of the ant colony optimization (ACO) algorithms for solving an inverse problem in non-destructive electromagnetic testing (NDET). The inverse problem, which consists in finding the shape and parameters of cracks in conducting plates starting from the signal of an eddy current testing (ECT) probe, is formulated as a discrete optimization problem. Two of the most widely known ant algorithms are adapted and applied to solve the optimization problem. The influence over the optimization algorithms performances of some problem specific local search strategies is also analyzed.

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Paper citation in several formats:
Duca, A. and Hameed, I. (2020). ACO Algorithms to Solve an Electromagnetic Discrete Optimization Problem. In Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-475-6 ISSN 2184-2825, pages 115-122. DOI: 10.5220/0009980001150122

@conference{ecta20,
author={Anton Duca. and Ibrahim Hameed.},
title={ACO Algorithms to Solve an Electromagnetic Discrete Optimization Problem},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2020},
pages={115-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009980001150122},
isbn={978-989-758-475-6},
issn={2184-2825},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - ACO Algorithms to Solve an Electromagnetic Discrete Optimization Problem
SN - 978-989-758-475-6
IS - 2184-2825
AU - Duca, A.
AU - Hameed, I.
PY - 2020
SP - 115
EP - 122
DO - 10.5220/0009980001150122

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