Autonomous Mission Planning for Multi-Agile Earth Observation Satellites Using Whale Optimization Algorithm

Gao, Xinzhou; Zhang, Haibo; Yu, Tianhang; Ma, Guorui; Gao, Duozhi

doi:10.1109/cac51589.2020.9326860

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…The algorithms in this paper are tested on problems of different scales the discrete artificial bee colony algorithm (DABC), the improved genetic algorithm (IGA) [17], and the improved discrete particle swarm algorithm (IDPSO) [40], respectively. Each algorithm is run 20 times on each problem scale to record the best fitness value (BF) and the average fitness value (AF), and the results are shown in Table 5.…”

Section: B Algorithm Comparisonmentioning

confidence: 99%

“…Zheng et al [16] designed iterative rules based on termination algebra and jump conditions by referring to the advantages of dynamic mutation strategy and adaptive mutation strategy, so as to overcome the shortcomings of traditional genetic algorithms, such as easy to fall into local optimization and long solving time. Gao et al [17] introduced the tabu search method and Metropolis rule into the mutation operation of genetic algorithm to accelerate the convergence speed of the algorithm and improve the probability of finding the optimal solution. Habet et al [10] mapped the satellite mission planning problem into a constraint satisfaction optimization problem and performed the construction of the taboo search neighborhood by partial enumeration based on insertion trial.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Discrete Artificial Bee Colony Algorithm for Imaging Satellite Mission Planning

Yang

Liu

2023

IEEE Access

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Imaging satellite mission planning has received more and more attention as one of the core problems in the field of imaging satellite applications. In this paper, a hybrid discrete artificial bee colony (HDABC) algorithm is proposed to address this problem. The HDABC algorithm improves the three search phases of the basic artificial bee colony (ABC) algorithm to make them applicable to the discrete satellite mission planning problem. In the employed bee search phase, the population is divided and a multi-strategy search equation mechanism is used to balance the exploration and development of the algorithm. In the following bee search phase, two kinds of neighborhood search operators are designed based on the problem characteristics to further improve the fitness values of the better solutions. In the scout bee search phase, a migration operator and an immigration operator are introduced to improve the fitness values of the worse solutions and promote the exchange of different subpopulations to achieve co-evolution. In the experimental part, orthogonal experimental design is used to determine the appropriate algorithm parameters. Simulation experiments are carried out to test problems of different sizes. The experimental results show that the proposed HDABC algorithm shows good performance.INDEX TERMS Artificial bee colony algorithm, imaging satellite, mission planning.

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Section: B Algorithm Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Discrete Artificial Bee Colony Algorithm for Imaging Satellite Mission Planning

Yang

Liu

2023

IEEE Access

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“…This combined approach is capable of addressing scenarios that prioritize both the efficiency and stability of satellite data transmission scheduling [12]. Collectively, these methodologies highlight the high efficiency of the GA and its potential for integration with other algorithms to address their inherent limitations, further enhancing their performance [13].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Algorithm for Ground Station Scheduling Problems

Xu,

Yu,

et al. 2024

Applied Sciences

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In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks.

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“…It often has the characteristics of non-linearity and high complexity. In real life, many problems can be expressed as high-dimensional optimization problems, such as large-scale job-shop-scheduling problems [ 1 ], vehicle-routing problems [ 2 ], feature selection [ 3 ], satellite autonomous observation mission planning [ 4 ], economic environmental dispatch [ 5 ], and parameter estimation. These kinds of optimization problems often greatly degrade the performance of the optimization algorithm as the dimension of the optimization problem increases, so it is extremely difficult to obtain the global optimal solution, which poses a technical challenge to solving many practical problems.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization

Liang

Wang

2023

Biomimetics

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With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm’s search abilities in terms of breadth and depth, the value of parameter G is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm’s solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm’s ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance.

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Autonomous Mission Planning for Multi-Agile Earth Observation Satellites Using Whale Optimization Algorithm

Cited by 3 publications

References 5 publications

A Hybrid Discrete Artificial Bee Colony Algorithm for Imaging Satellite Mission Planning

A Hybrid Discrete Artificial Bee Colony Algorithm for Imaging Satellite Mission Planning

A Hybrid Genetic Algorithm for Ground Station Scheduling Problems

An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization

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