Constrained differential evolution optimization for underwater glider path planning in sub-mesoscale eddy sampling

Abstract

This paper presents an approach for tackling constrained underwater glider path planning (UGPP), where the feasible path area is defined as a corridor around the border of an ocean eddy. The objective of the glider here is to sample the oceanographic variables more efficiently while keeping a bounded trajectory. Therefore, we propose a solution based on differential evolution (DE) algorithm mechanisms, including in its configuration self-adaptation of control parameters, population size reduction, ε-constraint handling with adjustment, and mutation based on elitistic best vector. Different aspects of this DE configuration are studied for the constrained UGPP challenge, on a prepared benchmark set comprised of 28 different specialized scenarios. The DE configurations were tested over a benchmark set over 51 independent runs for each DE configuration aspect. Comparison and suitability for the combination of these mechanisms is reported, through the per-scenario and aggregated statistical performance differences, including different constraint handling definition strategies, different DE mutation strategies' configurations, and population sizing parameterizations. Our proposed solution outranked all other compared algorithms, keeping a trajectory within the limits with 100% success rate in all physically feasible scenarios; on average, it improved the randomly initialized trajectories fitness by roughly 50%, even reaching perfect fitness (all-around, 360-degree eddy corridor sampling) in some scenarios.