Optimizing a Drifter Cast Strategy with a Genetic Algorithm

Abstract

An experiment design problem?-that of drifter cast strategy?-is discussed. Different optimization techniques used as part of preparations for the Semaphore-93 air-sea experiment, during which drifters were deployed, are examined. The oceanographic experiment objective was to sample a 500-km-square zone cantered at 33°N, 22°W in the Azores current area, using an average of 25 surface drifters for at least one month. We investigate different ?orders of merit? for determining the performance of a particular cast strategy, as well as the method of genetic algorithms for optimizing the strategy. Our technique uses dynamic reference knowledge of the area where the simulation takes place. Two reference sets were used: a steady-state field calculated with data collected from the Kiel University April 1982 hydrographic experiment, and data output from a regional quasigeostrophic model assimilating two years of Geosat altimetric data. The strategies obtained via the genetic algorithm method were compared with regular array drifter deployments, which is the intuitive and commonly used approach. It was also found that a cheap objective function gives strategies comparable to one which was more accurate but computationally expensive. Optimization by the genetic algorithm method is shown to be efficient and robust.