Multi-objective optimization of continuous monitoring scheduling for moving targets by earth observation satellites☆

作     者：Chao, Tao Han, Xiaofeng Li, Xiang Yang, Ming 

作者机构：Harbin Inst Technol Control & Simulat Ctr Harbin 150001 Peoples R China Harbin Inst Technol Sch Math Harbin 150001 Peoples R China 

出 版 物：《ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE》 (Eng Appl Artif Intell)

年 卷 期：2025年第144卷

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：national natural science foundation of china 

主　　题：Earth observation satellites Moving target Scheduling Multi-objective optimization Diversity Convergence 

摘      要：Continuous monitoring scheduling for moving targets by earth observation satellites is a crucial optimization problem in the field of artificial intelligence. In this scenario, for moving targets, extending the observation duration and increasing the capture times are important. However, few studies consider optimizing these two objectives simultaneously. In this paper, we present a novel methodology for this multi-objective optimization scenario. Firstly, a multi-objective optimization model is established. To address the complex Pareto front of the problem, we propose a decomposition-based, weight-adjusted multi-objective evolutionary algorithm that demonstrates strong convergence on the baseline model while further enhancing diversity through weight- adjustment techniques. Experimental results demonstrate that: 1) The proposed method achieves a trade-off solution set that simultaneously balances two objectives, and 2) in comparison to existing multi-objective optimization methods, the proposed algorithm outperforms the existing algorithms in terms of convergence and diversity.