Knowledge-transfer based genetic programming algorithm for multi-objective dynamic agile earth observation satellite scheduling problem
The multi-objective dynamic agile earth observation satellite scheduling problem (MO-DAEOSSP) aims to schedule a set of real-time arrival requests and form a reasonable observation plan to satisfy various criteria. According to the requirements in practical applications, the total profit and the ave...
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| Published in | Swarm and evolutionary computation Vol. 85; p. 101460 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.03.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2210-6502 |
| DOI | 10.1016/j.swevo.2023.101460 |
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| Abstract | The multi-objective dynamic agile earth observation satellite scheduling problem (MO-DAEOSSP) aims to schedule a set of real-time arrival requests and form a reasonable observation plan to satisfy various criteria. According to the requirements in practical applications, the total profit and the average image quality of scheduled requests are taken as optimization goals in this study. Compared to manually designed heuristics and iterative-based methods used in previous research, genetic programming based hyper heuristics (GPHH) can automatically evolve high-quality heuristic rules (HRs) for real-time scheduling without being highly dependent on expert knowledge. In this paper, a knowledge-transfer based multi-objective GPHH framework (KT-MOGP) is proposed, equipped with a heuristic-based simulation considering the idle monitoring, to evolve non-dominated HRs for solving MO-DAEOSSP. The heuristic-based simulation generates feasible schedules and returns fitness values for given HRs, which are the individuals evolved by KT-MOGP. KT-MOGP applies a knowledge transfer mechanism to accelerate convergence. Once a source problem is trained, its non-dominated solutions are extracted and their feature importance is transferred to guide the initialization of another target problem, by which the knowledge generated during the training process can be fully utilized. Experimental results on three sets of instances show that KT-MOGP outperforms the existing GPHH-based method and that the evolved HRs are competitive compared to several classical constructive heuristics and multi-objective evolutionary algorithms. The results also show the effectiveness of the proposed knowledge transfer-based initialization. To the best of our knowledge, this study is the first attempt to consider both multi-objective scenarios and real-time arrival requests. |
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| AbstractList | The multi-objective dynamic agile earth observation satellite scheduling problem (MO-DAEOSSP) aims to schedule a set of real-time arrival requests and form a reasonable observation plan to satisfy various criteria. According to the requirements in practical applications, the total profit and the average image quality of scheduled requests are taken as optimization goals in this study. Compared to manually designed heuristics and iterative-based methods used in previous research, genetic programming based hyper heuristics (GPHH) can automatically evolve high-quality heuristic rules (HRs) for real-time scheduling without being highly dependent on expert knowledge. In this paper, a knowledge-transfer based multi-objective GPHH framework (KT-MOGP) is proposed, equipped with a heuristic-based simulation considering the idle monitoring, to evolve non-dominated HRs for solving MO-DAEOSSP. The heuristic-based simulation generates feasible schedules and returns fitness values for given HRs, which are the individuals evolved by KT-MOGP. KT-MOGP applies a knowledge transfer mechanism to accelerate convergence. Once a source problem is trained, its non-dominated solutions are extracted and their feature importance is transferred to guide the initialization of another target problem, by which the knowledge generated during the training process can be fully utilized. Experimental results on three sets of instances show that KT-MOGP outperforms the existing GPHH-based method and that the evolved HRs are competitive compared to several classical constructive heuristics and multi-objective evolutionary algorithms. The results also show the effectiveness of the proposed knowledge transfer-based initialization. To the best of our knowledge, this study is the first attempt to consider both multi-objective scenarios and real-time arrival requests. |
| ArticleNumber | 101460 |
| Author | Chen, Ming Wan, Qian Xing, Lining Chen, Yuning Chen, Yingwu Song, Yanjie Wei, Luona |
| Author_xml | – sequence: 1 givenname: Luona surname: Wei fullname: Wei, Luona email: wlnelysion@163.com organization: College of Electronics and Information Engineering, South-Central Minzu University, Wuhan, 430074, China – sequence: 2 givenname: Ming surname: Chen fullname: Chen, Ming email: cmself@163.com organization: College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China – sequence: 3 givenname: Lining surname: Xing fullname: Xing, Lining email: xinglining@gmail.com organization: College of Electronic Engineering, Xidian University, Xi’an, 710126, China – sequence: 4 givenname: Qian surname: Wan fullname: Wan, Qian email: wanq8228@163.com organization: National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, 430079, China – sequence: 5 givenname: Yanjie orcidid: 0000-0002-4313-8312 surname: Song fullname: Song, Yanjie email: songyj_2017@163.com organization: National Defense University, Haidian District, Beijing, 100091, China – sequence: 6 givenname: Yuning surname: Chen fullname: Chen, Yuning email: cynnudt@hotmail.com organization: College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China – sequence: 7 givenname: Yingwu surname: Chen fullname: Chen, Yingwu email: ywchen@nudt.edu.cn organization: College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China |
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