Abstract
Multi-user agile earth observation satellite scheduling problem (MU-AEOSSP) is an important combinatorial optimization problem for satellite daily management. In this study, a MU-AEOSSP is addressed to tackle the failure rate and the fairness of different users simultaneously. A hybrid multi-objective coevolutionary approach (HMOCA) is then proposed to handle the complicate constraints and to optimize the objectives. HMOCA evolves two populations to solve an original MU-AEOSSP considering all constraints and a helper problem without the transition time constraint. By the cooperation of the two population, both the convergence and the diversity performance can be significantly improved. To further enhance the performance of HMOCA, several specific variation operators and a local search operator considering the time-dependent transition time of the MU-AEOSSP are equipped. The HMOCA is extensively tested and compared with three classical multi-objective evolutionary algorithms (NSGAII, MOEA/D, IBEA) and two methods of the time-dependent multi-objective AEOSSP (D-MOMA-TD and I-MOMA-TD) on several instances which are generated based on real-word situation. Experiment results show that the proposed approach outperforms all the comparison methods on most of the instances in terms of convergence, solution quality and diversity.
Supported by the National Natural Science Foundation of China, Grant No. 71701203, 72001212 and 71901213.
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Wei, L., Song, Y., Xing, L., Chen, M., Chen, Y. (2022). A Hybrid Multi-objective Coevolutionary Approach for the Multi-user Agile Earth Observation Satellite Scheduling Problem. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1565. Springer, Singapore. https://doi.org/10.1007/978-981-19-1256-6_18
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