Autonomous Task Planning Method for Multi-Satellite System Based on a Hybrid Genetic Algorithm

Long, Jun; Wu, Shimin; Han, Xiao; Wang, Yunbo; Liu, Limin

doi:10.3390/aerospace10010070

Cited by 9 publications

(6 citation statements)

References 34 publications

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“…In addition, although the experimental satellite in this paper is only GF3, the proposed MOEA can also be applied to multi-satellite optimization problems, for example, constellation optimization [48], multi-satellite joint scheduling [49], etc. For different application problems, the goal of an MOEA is always to find the best solution to the problem.…”

Section: Discussionmentioning

confidence: 99%

Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping

Chen,

Shen,

Zhang

et al. 2023

Remote Sensing

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With satellite quantity and quality development in recent years, remote sensing products in vast areas are becoming widely used in more and more fields. The acquisition of large regional images requires the scientific and efficient utilization of satellite resources through imaging satellite task planning technology. However, for imaging satellite task planning in a vast area, a large number of decision variables are introduced into the imaging satellite task planning model, making it difficult for existing optimization algorithms to obtain reliable solutions. This is because the search space of the solution increases the exponential growth with the increase in the number of decision variables, which causes the search performance of optimization algorithms to decrease significantly. This paper proposes a large-scale multi-objective optimization algorithm based on efficient competition learning and improved non-dominated sorting (ECL-INS-LMOA) to efficiently obtain satellite imaging schemes for large areas. ECL-INS-LMOA adopted the idea of two-stage evolution to meet the different needs in different evolutionary stages. In the early stage, the proposed efficient competitive learning particle update strategy (ECLUS) and the improved NSGA-II were run alternately. In the later stage, only the improved NSGA-II was run. The proposed ECLUS guarantees the rapid convergence of ECL-INS-LMOA in the early evolution by accelerating particle update, introducing flight time, and proposing a binary competitive swarm optimizer BCSO. The results of the simulation imaging experiments on five large areas with different scales of decision variables show that ECL-INS-LMOA can always obtain the imaging satellite mission planning scheme with the highest regional coverage and the lowest satellite resource consumption within the limited evaluation times. The experiments verify the excellent performance of ECL-INS-LMOA in solving vast area mapping planning problems.

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Section: Discussionmentioning

confidence: 99%

Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping

Chen,

Shen,

Zhang

et al. 2023

Remote Sensing

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“…Implementing GA alone is usually inadequate, especially in the face of complex problems with many obstacles. The biggest limitation of genetic algorithms is that they cannot achieve a global optimum like heuristic methods and their optimisation time is long (Long et al, 2023;Yusran et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The combination of GA with another heuristic method can potentially increase the model's ability to achieve global optimal solutions. Hybrid methods consisting of GA and other optimisation algorithms can significantly improve the results, making them better than single algorithms (Boonyopakorn & Meesad, 2017;Chen et al, 2023;Long et al, 2023;Omar et al, 2018). Furthermore, the GA is already being widely used to tackle global optimisation problems (Elaziz et al, 2023;Houssein et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction

Musa,

Ku-Mahamud,

Sardi Salim

et al. 2024

JICT

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Energy consumption planning of an area is very important. It is essential to accurately predict the amount of short-term power required by an area using a highly effective prediction technique. The real-value-genetics-algorithm (RVGA) is the most effective technique that is currently used. However, the RVGA has some drawbacks, including the fact that it gets caught in premature convergence even when the search is performed over long iterations. This study proposes a hybrid prediction algorithm which comprises the RVGA and the extended-Nelder-Mead (ENM) algorithm. The ENM was implemented to speed up the search for the best among all solutions produced by the RVGA. The RVGA was configured to run under small iterations, and the ENM was used to achieve convergence. Experiments were performed on historical datasets containing the monthly electricity demand of the Gorontalo area, a region in Indonesia. The performance of the hybrid algorithm was compared to the hybrid Genetic Algorithm-Particle Swarm Optimisation (GA-PSO) and Real Coded-Genetic Algorithm (RC-GA) energy demand models based on the mean-absolute-percentage-error (MAPE), mean-square-error (MSE), root-mean-square-error (RMSE), and mean-absolute-deviation (MAD) error rates. The results showed that the proposed hybrid algorithm’s MAPE, MSE, RMSE, and MAD errors were 2.95 percent, 0.13 percent, 0.36 percent and 1.29 percent, respectively. Based on the accuracy measure obtained from this study, it implies that the RVGAENM hybrid is the best model for forecasting monthly electricity demand.

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“…Multi-satellite imaging mission planning (MSIMP) refers to the optimal allocation of limited satellite resources based on a complex mission environment and varied user requirements to optimize Earth observation operation efficiency and gather remote sensing images [1], [2]. Multiple imaging satellites can achieve long-term and multi-directional continuous monitoring of the observation area through mutual cooperation, and they play an important role in geographical mapping, land resource survey, disaster monitoring, and other fields [3], [4]. However, during the implementation of satellite missions, the environment dynamically changes, influencing a large range…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Local Replanning Strategy for Multi-Satellite Imaging Mission Planning in Uncertain Environments

Yang,

Hu,

Huang

et al. 2023

IEEE Access

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Multi-satellite imaging mission planning (MSIMP) research has advanced substantially in recent years. However, contemporary MSIMP research in uncertain environments is still confronting challenges such as loss of satellite resource allocation, inadequate anti-jamming ability of the mission planning scheme, and low mission completion rate. Therefore, in this work, we propose a hybrid local replanning strategy improved adaptive differential evolutionary (HLRS-MSFADE) algorithm based on the multi-satellite imaging mission planning in uncertain environments (MSIMPUE). First, an MSIMPUE model based on uncertainty assessment is constructed. This model solves the problem of assessing new tasks with varied qualities to decide the observation order in an uncertain environment and decreases the loss caused by inefficient satellite resource allocation. Second, to address the issue of difficulty in planning for changing new task requirements in uncertain environments, an HLRS for uncertain environments is developed to ensure efficient task insertion while avoiding conflict costs. Finally, an MSFADE algorithm is presented to handle the problem of long MSIMPUE mission response time and low mission completion rate with good quality in an acceptable computation time. The simulation results validated the effectiveness and stability of the method in dealing with MSIMPUE. Moreover, the HLRS-MSFADE algorithm outperforms previous methods in terms of mission response time, mission completion rate, and motion perturbation.INDEX TERMS Uncertain environment, multi-satellite imaging mission planning, replanning strategy, adaptive differential evolutionary algorithm.

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Autonomous Task Planning Method for Multi-Satellite System Based on a Hybrid Genetic Algorithm

Cited by 9 publications

References 34 publications

Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping

Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping

Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction

A Hybrid Local Replanning Strategy for Multi-Satellite Imaging Mission Planning in Uncertain Environments

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