Observation scheduling problem for AEOS with a comprehensive task clustering

Abstract

Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite (AEOS) together, a comprehensive task clustering (CTC) is proposed to improve the observation scheduling problem for AEOS (OSP-FAS). Since the observation scheduling problem for AEOS with comprehensive task clustering (OSWCTC) is a dynamic combination optimization problem, two optimization objectives, the lossrate (LR) of the image quality and the energy consumption (EC), are proposed to format OSWCTC as a bi-objective optimization model. Harnessing the power of an adaptive large neighborhood search (ALNS) algorithm with a nondominated sorting genetic algorithm II (NSGA-II), a bi-objective optimization algorithm, ALNS+NSGA-II, is developed to solve OSWCTC. Based on the existing instances, the efficiency of ALNS+NSGA-II is analyzed from several aspects, meanwhile, results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.