TY - JOUR
T1 - A Multi-Objective Enhanced Estimation of Distribution Algorithm for Proactive Scheduling of Agile Earth Observation Satellites
AU - Zhang, Xinyu
AU - Yin, Hongyu
AU - Deng, Yixin
AU - Zhang, Yisi
AU - Sun, Tianyu
AU - Liu, Xiaoze
AU - Wu, Chu Ge
AU - Cui, Shisheng
N1 - Publisher Copyright:
Copyright © 2025 The Authors.
PY - 2025/8/1
Y1 - 2025/8/1
N2 - In this paper, an Enhanced Estimation of Distribution Algorithm (EDA) for the multi-objective problem is proposed for the active scheduling of agile Earth observation satellites considering external environmental disturbances and emergency mission insertion. Firstly, considering that the increased idle time can enhance the fault tolerance of the system, two metrics are proposed: schedule profit and total slack time, based on which a multiple multidimensional knapsack problem with conflicts problem is modeled. Secondly, in order to solve the problem of multi-objective model, EDA is combined with non-dominating sorting strategy module to store and screen out elite solutions during the evolution process. Finally, EDA is compared with NSGA-II by combining STK simulation data, and it is proved that the improved EDA can achieve better distribution and convergence in obtaining the Pareto optimal set. This study provides methodological support for active scheduling of agile Earth observation satellites.
AB - In this paper, an Enhanced Estimation of Distribution Algorithm (EDA) for the multi-objective problem is proposed for the active scheduling of agile Earth observation satellites considering external environmental disturbances and emergency mission insertion. Firstly, considering that the increased idle time can enhance the fault tolerance of the system, two metrics are proposed: schedule profit and total slack time, based on which a multiple multidimensional knapsack problem with conflicts problem is modeled. Secondly, in order to solve the problem of multi-objective model, EDA is combined with non-dominating sorting strategy module to store and screen out elite solutions during the evolution process. Finally, EDA is compared with NSGA-II by combining STK simulation data, and it is proved that the improved EDA can achieve better distribution and convergence in obtaining the Pareto optimal set. This study provides methodological support for active scheduling of agile Earth observation satellites.
KW - Agile earth observation satellite
KW - Estimation of distribution algorithm
KW - Multi-objective optimization
KW - Non-dominating sorting strategy
KW - STK simulation
UR - https://www.scopus.com/pages/publications/105025967515
U2 - 10.1016/j.ifacol.2025.11.426
DO - 10.1016/j.ifacol.2025.11.426
M3 - Conference article
AN - SCOPUS:105025967515
SN - 2405-8963
VL - 59
SP - 1836
EP - 1841
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
IS - 20
T2 - 23th IFAC Symposium on Automatic Control in Aerospace, ACA 2025
Y2 - 2 August 2025 through 6 August 2025
ER -