Task planning of space debris removal based on a hierarchical exploration artificial bee colony algorithm

Space debris poses a potentially catastrophic risk to large-scale LEO constellations due to its substantial cascading effects, thereby rendering efficient and timely debris removal a critical research area in contemporary aerospace engineering. This paper delves into the application of the three-dimensional traveling salesman problem with orbital transfer constraints for space debris removal task planning. To overcome the challenges of sparse optimal solutions and vulnerability to local optima in addressing large-scale space debris removal task planning problems, we introduce a new Hierarchical Exploration Artificial Bee Colony (HEABC) optimization algorithm. Initially, we present an innovative two-step search strategy that employs a short-and-long term hybrid approach to optimize search time utilization and enhance solution quality, thereby addressing the sparsity of optimal solutions in the HEABC algorithm. Subsequently, to mitigate the issue of converging to local optima in high-dimensional encoding-based search problems, we devise a dual population update strategy aimed at preserving the innate evolutionary driving force of the search population. This strategy ensures the continuous updating of the population, even in the absence of intrinsic driving forces. Ultimately, experimental results substantiate that our proposed HEABC algorithm attains superior task planning sequences in a reduced time span and exhibits heightened adaptability in comparison to various traditional search algorithms. This is corroborated by numerical experiments conducted on one publicly available and one STK-generated space debris datasets.