Satellite range scheduling, a multi-constrained combinatorial optimization problem, is crucial to guaranteeing the normal operation and application of onboard satellites. Traditional methods are dedicated to finding one optimal schedule, having ignored the problem may process multiple high-quality schedules. To provide a set of alternative schedules while maintaining the solution quality, we propose a co-evolutionary algorithm with elite archive strategy (COEAS) in this article. In COEAS, two populations are evolved to solve the original and relaxed problem in terms of schedule quality and diversity, respectively. During the evolution, the populations maintain a weak cooperation and only share the information in offspring combination phase. Further, an elite archive strategy is derived to identify and preserve potential stagnated and optimal individuals. In this strategy, the promising individuals would further participate in parent mating and offspring replacement for the dual purpose of maintaining potential optima recovery and fine-tuning the population. The experimental results show that the proposed algorithm is better than comparison algorithms in terms of efficacy (obtaining higher quality schedule), diversity (locating more optimal schedules) and flexibility (providing better alternatives).

Geosynchronous Synthetic Aperture Radar (GEO SAR) has a very long Coherent Processing Interval (in the order of hundreds of seconds) compared with other SAR platforms. Thus, the current methods of rotation effect matching and ship imaging that operate within a relatively short Coherent Processing Interval (in the order of seconds) are obviously not applicable. To address this problem, a novel ship imaging method with multiple sinusoidal functions matching for rotation effects is proposed for GEO SAR. Firstly, the influence of the rotational motion of a ship on the slant range is analyzed. It can be matched with the sum of multiple sinusoidal functions, and the signal model of a ship with rotational motion is given. Then, multiple sinusoidal functions for the matching-based ship imaging method are proposed, and their procedures are presented as follows: (1) The Generalized Keystone Transform and Generalized Dechirp Process (GKTGDP) is modified to compensate for the range migration and phase caused by the motion of GEO SAR. Then, the signal is focused at the frequencies of sinusoidal functions, and the frequencies can be matched. (2) From the matched frequencies, the other parameters of sinusoidal functions can be matched by parameter searching. (3) Based on the matched results, the Back Projection Algorithm (BPA) is used to take an image of the ship with rotational motion. Finally, the effectiveness of the proposed method is verified by numerical experiments.

In many-objective optimization, maintaining a good balance between convergence and diversity have turned out to be a considerable challenge for classical evolutionary algorithms. The large scale solution set required to describe the entire Pareto optimal front hinder the decision makers from finding the most satisfactory solution, whereas he/she is only interested in a limited part of the objective space. The dilemma can be handled by incorporating the preference information in the search process. In this paper, an evolutionary algorithm based on region preference is proposed for many-objective optimization. The preference model is constructed in combination of the target region and reference points. To focus the search on the preference region while maintaining well convergence and diversity within the region, a tri-level ranking criterion is introduced into the proposed algorithm, and different rank works at different phase of the search process. A fuzzy theory-based interactive approach is proposed to guide more individuals to further search into the objective space with higher preference degree and help the decision maker choose the most interested solution. The proposed algorithm has been extensively compared with other state-of-the-art preference-based algorithms on DTLZ1~ DTLZ4 test problems having 3–10 objectives. The experimental results indicate that the proposed algorithm can achieve competitive and better performance. Moreover, we extend the algorithm to handle multiple target regions.

Most existing evolutionary approaches to satellite range scheduling seek optimal solution in terms of the request satisfaction. The scheduling demand of managing ground station resource is seldom considered, which restricts their real-world applications. To effectively generate a set of more rational satellite range schedules, this paper establishes the multi-objective satellite range scheduling mathematical model. Unlike existing approaches, we propose a general population generation approach to solve the problem without relying on any specific kind of evolutionary algorithm, so different types of evolutionary algorithms can be extended to satellite range scheduling without modifying the original framework and search strategy. The idea is to utilize the request satisfaction and resource utilization knowledge learnt from parent schedules to guide the generation and updating of new solutions. Furthermore, an iterative rewriting operator is designed to guide a biased faster convergence towards the low request failure region in objective space. The proposed approach has been applied to five different types of classical and state-of-the-art evolutionary algorithms and examined on benchmark problems. Experimental results illustrate the search efficiency enhancement and good adaptability to different evolutionary algorithms, which show the broad application prospect for satellite range scheduling.

With the rapid development of communications technology, the space-based information network (SBIN) is increasingly threatened by the outside world. Dynamic changes in any part of its interior can cause the collapse of the entire network. Therefore, research on the invulnerability of SBIN has become an urgent need to promote the economic development of our country and improve the living standards of our people. To this end, this paper has carried out research on the node-attacked invulnerability of SBIN based on the complex network theory. First, based on the model of SBIN, the internal parameters of the network are analyzed theoretically based on complex networks. Second, the paper proposes an improved tree attack strategy to analyze the invulnerability of SBIN, which constitutes a problem where the traditional attack strategy has a low invulnerability and the connected edge cannot fully realize the network function. Then, based on the improved tree attack strategy algorithm, this paper optimizes the invulnerability of SBIN by constructing four different edge-increasing strategies. Through the research, the LDF edge-increasing strategy makes the entire network flatter and can effectively improve the network’s ability to resist destruction. The research of invulnerability based on the complex network has a certain technical support and theoretical guidance for the construction of a reasonable and stable SBIN.