Over the past few decades, there has been a significant increase in the number of Earth observation satellites, and the area of ground targets requiring observation has also been expanding. To effectively utilize the capabilities of these satellites and capture larger areas of ground targets, it has become essential to plan imaging tasks for large regional coverage using multiple satellites. First, we establish a 0-1 integer programming model to accurately describe the problem and analyze the challenges associated with solving the model. Second, we propose a heuristic algorithm based on the triple grids method. This approach utilizes a generated grid to create fewer candidate strips, a calculation grid to determine the effective coverage area more accurately, and a refined grid to solve the issue of repeated coverage of strips. Furthermore, we employ an approximation algorithm to further improve the solutions obtained from the heuristic algorithm. By comparing the proposed method to the traditional greedy heuristic algorithm and three evolutionary algorithms, the results show that our method has better performance in terms of coverage and efficiency.

Multi-satellite joint regional coverage aims to select the optimal combination of satellite resources to acquire the image information of the specified area. Meanwhile, more than three objectives are usually considered simultaneously during this process. Therefore, it is a typical many-objective optimization problem that is NP-hard. Most existing many-objective optimization algorithms cannot preserve extreme solutions due to the failure of Pareto dominance. In this paper, through introducing the idea of S-CDAS into the traditional NSGA-III, an improved many-objective evolutionary algorithm named NSGA-III for extreme solutions preservation (ESP-NSGA-III) is proposed with problem-specific genetic operations to generate regional coverage schemes. A comparative study is conducted with other six state-of-the-art many-objective evolutionary algorithms. Hypervolume (HV) and pure diversity (PD) metrics are used to evaluate the performance of algorithms. The simulation results show that ESP-NSGA-III has good comprehensive performance and improves the diversity of original algorithms. More importantly, the proposed algorithm can preserve extreme solutions during iteration so that satisfactory regional coverage scheme can be obtained. Our proposed methods are not only applicable to regional coverage tasks, but also have important reference significance for solving other real-world problems.

The problems of SVM include easy to fall into local optimum, repeated oscillation and slow convergence. Because:(1) mutation or local mutation of cloud evolutionary algorithm; (2) randomness and ambiguity of cloud model; (3) expected Ex control certainty of the optimization process, entropy En and super entropy He control uncertainty of the optimization process, these advantages make the cloud evolutionary algorithm can effectively avoid the above problems. Therefore, this paper proposes an analog circuit fault diagnosis method based on cloud evolutionary algorithm optimization support vector machine (CBEA-SVM) to solve these problem. CBEA is used to find penalty factor and kernel function variance of the support vector machine, SVM optimized by CBEA is used for fault classification. Using a Sallen-key bandpass filter and a CTSV filter circuit as circuits to be tested, the diagnostic results show that the diagnostic accuracy of CBEA-SVM has reached more than 98%, and it proves that CBEA can avoid the problems of traditional optimization algorithms.