This study investigates the scheduling problem ofmultiple agile optical satelliteswith large-scale tasks.This problem is difficult to solve owing to the time-dependent characteristic of agile optical satellites,comple...This study investigates the scheduling problem ofmultiple agile optical satelliteswith large-scale tasks.This problem is difficult to solve owing to the time-dependent characteristic of agile optical satellites,complex constraints,and considerable solution space.To solve the problem,we propose a scheduling method based on an improved sine and cosine algorithm and a task merging approach.We first establish a scheduling model with task merging constraints and observation action constraints to describe the problem.Then,an improved sine and cosine algorithm is proposed to search for the optimal solution with the maximum profit ratio.An adaptive cosine factor and an adaptive greedy factor are adopted to improve the algorithm.Besides,a taskmerging method with a task reallocation mechanism is developed to improve the scheduling efficiency.Experimental results demonstrate the superiority of the proposed algorithm over the comparison algorithms.展开更多
基金supported by Science and Technology on Complex Electronic System Simulation Laboratory (Funding No.6142401003022109).
文摘This study investigates the scheduling problem ofmultiple agile optical satelliteswith large-scale tasks.This problem is difficult to solve owing to the time-dependent characteristic of agile optical satellites,complex constraints,and considerable solution space.To solve the problem,we propose a scheduling method based on an improved sine and cosine algorithm and a task merging approach.We first establish a scheduling model with task merging constraints and observation action constraints to describe the problem.Then,an improved sine and cosine algorithm is proposed to search for the optimal solution with the maximum profit ratio.An adaptive cosine factor and an adaptive greedy factor are adopted to improve the algorithm.Besides,a taskmerging method with a task reallocation mechanism is developed to improve the scheduling efficiency.Experimental results demonstrate the superiority of the proposed algorithm over the comparison algorithms.
