Dual-Population Artificial Bee Colony Algorithm for Joint Observation Satellite Mission Planning Problem

作     者：Jiang, Xuemei Song, Yanjie Xing, Lining 

作者机构：Xidian Univ Sch Elect Engn Xian 710126 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2022年第10卷

页      面：28911-28921页

核心收录：

基　　金：National Natural Science Foundation of China Special Projects in Key Fields of Universities in Guangdong [2021ZDZX1019] Hunan Provincial Innovation Foundation for Postgraduate [CX20200585] 

主　　题：Satellites Task analysis Planning Statistics Sociology Earth Artificial bee colony algorithm Observation satellite dual population artificial bee colony algorithm planning heuristic 

摘      要：Earth observation satellites provide services for users by taking images. The rapid increase in the number of satellites and missions makes mission planning more difficult. In order to solve this problem, this paper adopts a multi-population evolutionary algorithm. First, a mixed-integer programming model of the joint observation satellites mission planning problem (JOSMPP) is constructed. After that, a dual-population artificial bee colony algorithm (DPABC) and a heuristic task scheduling algorithm are proposed. Two learning-based nectar generation methods are used to guide the direction of population optimization. The search population is used to lead the nectar search at the stage of employed bees and supplement population is used to improve search performance. According to the performance of these two populations, candidate populations are generated to realize the search of onlooker bees. After each generation of population search, the composition of the two populations is adjusted according to the performance. Finally, the experimental results show that the DPABC algorithm has more advantages than multiple state-of-the-art algorithms to solve the joint mission planning problem of earth observation satellites.