Using Evolutionary Algorithms to Plan Automatic Minehunting Operations

Nuno Abreu, Aníbal Matos

Abstract

While autonomous underwater vehicles (AUVs) are increasingly being used to perform mine countermeasures (MCM) operations, the capability of these systems is limited by the efficiency of the planning process. In this paper we study the problem of multiobjective MCM mission planning with an AUV. In order to overcome the inherent complexity of the problem, a multi-stage algorithm is proposed and evaluated. Our algorithm combines an evolutionary algorithm (EA) with a local search procedure based on simulated annealing (SA), aiming at a more flexible and effective exploration and exploitation of the search space. An artificial neural network (ANN) model was also integrated in the evolutionary procedure to guide the search. The results show that the proposed strategy can efficiently identify a higher quality solution set and solve the mission planning problem.

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Paper Citation


in Harvard Style

Abreu N. and Matos A. (2014). Using Evolutionary Algorithms to Plan Automatic Minehunting Operations . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 228-235. DOI: 10.5220/0005043102280235


in Bibtex Style

@conference{icinco14,
author={Nuno Abreu and Aníbal Matos},
title={Using Evolutionary Algorithms to Plan Automatic Minehunting Operations},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={228-235},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005043102280235},
isbn={978-989-758-039-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Using Evolutionary Algorithms to Plan Automatic Minehunting Operations
SN - 978-989-758-039-0
AU - Abreu N.
AU - Matos A.
PY - 2014
SP - 228
EP - 235
DO - 10.5220/0005043102280235