The Evaluation of Multi-Objective Evolutionary Algorithms for a Maritime Domain Awareness Problem
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Illegal, unreported and unregulated (IUU) fishing is a worldwide issue causing local and global financial losses, depleting natural resources and causing undue pressure upon the fishing industry. It is estimated that IUU fishing accounts for about 30% of all fishing activity worldwide, both on open oceans and within national exclusive economic zones. Responding to IUU fishing incidents is of paramount importance to law enforcement and marine environment protection organizations. We employ an optimization approach to the IUU problem by applying Evolutionary Multi-Objective Optimization solution techniques (EMOO) to automatically generate a set of promising candidate responses once an IUU fishing event has been identified. Four of the most frequently cited EMOO algorithms were used to explore the trade-off among three conflicting decision objectives, namely: the proximity to the IUU fishing vessel, the total cost of the response and the probability of confirming the detection, which is important for prosecution purposes.
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Copyright © 2019 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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akinbulire-theevaluationofmultiobjectiveevolutionary.pdf | 2023-05-05 | Public | Download |