Constrained differential evolution optimization for underwater glider path planning in sub mesoscale eddy sampling
Graphical abstractDisplay Omitted HighlightsAn approach for tackling constrained underwater glider sub mesoscale path planning.The feasible path area is defined as a corridor around the border of an ocean eddy.A new configuration of constrained differential evolution algorithm and mechanisms.A new benchmark set with 28 different specialized scenarios defines the challenge.Per scenario and aggregated performance analyses of different mechanism's new configurations. 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, 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.
Appl. Soft Comput.