A Heuristic Genetic Algorithm for Regional Targets’ Small Satellite Image Downlink Scheduling Problem

Abstract

Small satellite image downlink scheduling problem (SSIDSP) is an important part of satellite mission planning. SSIDSP mainly needs to balance how to better match the limited receiving capacity of the ground station with the limited satellite resources. In this paper, regional targets are considered with SSIDSP. We propose a mathematical model that maximizes profit by considering time value and regional targets. A downlink schedule algorithm (DSA) is proposed to complete the task sequence arrangement and generate scheduling results. A heuristic genetic algorithm (HGA) is used to optimize the generated task sequence to achieve higher profit. Three scale test instances are used to test the effectiveness of HGA and DSA. We compare the effect of HGA, basic genetic algorithm (GA), and construction heuristic algorithm. The experimental results proved that the proposed approach ensures the successful completion of observation tasks and is effective for SSIDSP.

A Satellite Observation Data Transmission Scheduling Algorithm Oriented to Data Topics

“…Since the satellite data transmission scheduling problem has been proven to be NP hard, the exact search algorithms such as exhaustion algorithm and branch-and-bound algorithm are unable to get satisfied results in a short time. The 6 International Journal of Aerospace Engineering intelligent optimization algorithms, one kind of stochastic search algorithms, have been widely adopted in many NP hard problems [27], such as 0-1 knapsack problems, workshop scheduling problems, and traveling salesman problems. Particle swarm optimization algorithm (PSO) and genetic algorithm (GA) are two important intelligent optimization algorithms.…”

“…Chen et al [2,24,25] employed improved genetic algorithm and particle swarm optimization algorithm to handle the satellite data transmission scheduling problem with some specific requirements, for incremental observation tasks, member satellites of the same cluster, and real-time and playback data transmission modes. Addressing the satellite image downlink scheduling problem, Yao et al [26] and Song et al [27] brought up a heuristic genetic algorithm.…”

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“…Many computational algorithms have been proposed to solve the multi-satellites mission scheduling problem, which can be mainly divided into three categories, exact algorithms [17], heuristic algorithms [18][19][20] and metaheuristic algorithms [21][22][23]. Exact algorithms are used to find the optimal solution accurately [17].…”

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“…Cho et al described a two-step binary linear programming formula considering energy consumption, which solved the downlink scheduling sub-problem first and, then, the constellation task scheduling problem [25]. Song et al considered that all observation data in the same area need to be downloaded to the same ground station, and proposed the construction heuristic algorithm and downlink scheduling algorithm [26]. Xiao et al considered the impact of weather uncertainty on mission success to ensure the maximum reliability of mission scheduling, proposed two planning modes based on periodic triggering and event triggering, and adopted a two-stage scheduling scheme to optimize observation and downlink operation simultaneously [27].…”