.png){kind=icon}
返回
Evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3197637.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Most existing evolutionary approaches to satellite range scheduling seek optimal solution in terms of the request satisfaction. The scheduling demand of managing ground station resource is seldom considered, which restricts their real-world applications. To effectively generate a set of more rational satellite range schedules, this paper establishes the multi-objective satellite range scheduling mathematical model. Unlike existing approaches, we propose a general population generation approach to solve the problem without relying on any specific kind of evolutionary algorithm, so different types of evolutionary algorithms can be extended to satellite range scheduling without modifying the original framework and search strategy. The idea is to utilize the request satisfaction and resource utilization knowledge learnt from parent schedules to guide the generation and updating of new solutions. Furthermore, an iterative rewriting operator is designed to guide a biased faster convergence towards the low request failure region in objective space. The proposed approach has been applied to five different types of classical and state-of-the-art evolutionary algorithms and examined on benchmark problems. Experimental results illustrate the search efficiency enhancement and good adaptability to different evolutionary algorithms, which show the broad application prospect for satellite range scheduling.
Keyword:
Satellites
Search problems
Scheduling
Evolutionary computation
Statistics
Learning systems
Satellite range scheduling
multi-objective optimization
evolutionary algorithm
learning-guided
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏Cytogenetic Effects of Gamma Rays and Ethyl Methane Sulphonate in Chilli Pepper (Capsicum annuum L.)..png)
CYTOLOGIA
OAAI
CYTOLOGIAIF0
OAAI分享收藏PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台OAAI
OAAI分享收藏Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏PLA2R POSITIVE MEMBRANOUS NEPHROPATHY AFTER ANAPLASMOSIS-INDUCED HLH: A COINCIDENCE OR ASSOCIATION?PLA2R阳性膜性肾病与无形体病诱导的HLH：是巧合还是关联？
PREAI
分享收藏Optimization-based scheduling for the single-satellite, multi-ground station communication problem基于优化的单卫星多地面站通信调度问题
PREAI
分享收藏
查看更多内容