  Search and detection of objects on the ocean surface is a challenging task
due to the complexity of the drift dynamics and lack of known optimal solutions
for the path of the search agents. This challenge was highlighted by the
unsuccessful search for Malaysian Flight 370 (MH370) which disappeared on March
8, 2014. In this paper, we propose an improvement of a search algorithm rooted
in the ergodic theory of dynamical systems which can accommodate complex
geometries and uncertainties of the drifting search areas on the ocean surface.
We illustrate the effectiveness of this algorithm in a computational
replication of the conducted search for MH370. In comparison to conventional
search methods, the proposed algorithm leads to an order of magnitude
improvement in success rate over the time period of the actual search
operation. Simulations of the proposed search control also indicate that the
initial success rate of finding debris increases in the event of delayed search
commencement. This is due to the existence of convergence zones in the search
area which leads to local aggregation of debris in those zones and hence
reduction of the effective size of the area to be searched.
