Knowledge-transfer based genetic programming algorithm for multi-objective dynamic agile earth observation satellite scheduling problem
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- @Article{WEI:2024:swevo,
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author = "Luona Wei and Ming Chen and Lining Xing and
Qian Wan and Yanjie Song and Yuning Chen and Yingwu Chen",
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title = "Knowledge-transfer based genetic programming algorithm
for multi-objective dynamic agile earth observation
satellite scheduling problem",
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journal = "Swarm and Evolutionary Computation",
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volume = "85",
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pages = "101460",
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year = "2024",
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ISSN = "2210-6502",
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DOI = "doi:10.1016/j.swevo.2023.101460",
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URL = "https://www.sciencedirect.com/science/article/pii/S2210650223002328",
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keywords = "genetic algorithms, genetic programming, Agile
satellite scheduling, Multi-objective, Knowledge
transfer",
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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 used. 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",
- }
Genetic Programming entries for
Luona Wei
Ming Chen
Lining Xing
Qian Wan
Yanjie Song
Yuning Chen
Yingwu Chen
Citations