Illegal, unreported and unregulated (IUU) fishing is largely responsible for dwindling fish stocks and marine habitat destruction. 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. This paper employs Evolutionary Multi-Objective Optimization (EMOO) to automatically generate a set of promising candidate responses once an IUU fishing event has been identified. Four EMOO algorithms will explore the trade-off among three conflicting decision objectives, namely (1) the proximity to the target (IUU fishing vessel), (2) the total cost of the response for all engaged assets and (3) the probability of confirming the detection of the offending vessel inside the fishing zone, which is important for prosecution purposes. We illustrate the proposed methodology with a simulated scenario along the Canadian Atlantic coast and discuss some of the automatically generated responses that are offered to the decision maker for their consideration. To the best of our knowledge, this is the first time EMOO techniques have been applied to respond to IUU fishing incidents.

dx.doi.org/10.1109/CIVEMSA.2018.8439981
23rd Annual IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2018
Department of Systems and Computer Engineering

Akinbulire, T. (Tolulope), Falcon, R. (Rafael), Abielmona, R. (Rami), & Schwartz, H.M. (2018). Responding to illegal, unreported and unregulated fishing with evolutionary multi-objective optimization. In CIVEMSA 2018 - 2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Proceedings. doi:10.1109/CIVEMSA.2018.8439981