Guangming Dai

Heuristic Scheduling Algorithm Oriented Dynamic Tasks for Imaging Satellites

Imaging satellite scheduling is an NP-hard problem with many complex constraints. This paper researches the scheduling problem for dynamic tasks oriented to some emergency cases. After the dynamic properties of satellite scheduling were analyzed, the optimization model is proposed in this paper. Based on the model, two heuristic algorithms are proposed to solve the problem. The first heuristic algorithm arranges new tasks by inserting or deleting them, then inserting them repeatedly according to the priority from low to high, which is named IDI algorithm. The second one called ISDR adopts four steps: insert directly, insert by shifting, insert by deleting, and reinsert the tasks deleted. Moreover, two heuristic factors, congestion degree of a time window and the overlapping degree of a task, are employed to improve the algorithm’s performance. Finally, a case is given to test the algorithms. The results show that the IDI algorithm is better than ISDR from the running time point of view while ISDR algorithm with heuristic factors is more effective with regard to algorithm performance. Moreover, the results also show that our method has good performance for the larger size of the dynamic tasks in comparison with the other two methods.

A Novel Technique to Compute the Revisit Time of Satellites and Its Application in Remote Sensing Satellite Optimization Design

This paper proposes a novel technique to compute the revisit time of satellites within repeat ground tracks. Different from the repeat cycle which only depends on the orbit, the revisit time is relevant to the payload of the satellite as well, such as the tilt angle and swath width. The technique is discussed using the Bezout equation and takes the gravitational second zonal harmonic into consideration. The concept of subcycles is defined in a general way and the general concept of “small” offset is replaced by a multiple of the minimum interval on equator when analyzing the revisit time of remote sensing satellites. This technique requires simple calculations with high efficiency. At last, this technique is used to design remote sensing satellites with desired revisit time and minimum tilt angle. When the side-lap, the range of altitude, and desired revisit time are determined, a lot of orbit solutions which meet the mission requirements will be obtained fast. Among all solutions, designers can quickly find out the optimal orbits. Through various case studies, the calculation technique is successfully demonstrated.

Cooperative coevolution with dependency identification grouping for large scale global optimization

Large scale optimization is a very challenging task in optimization area. The variable interaction in non-separable problems is a primary source of performance loss, especially for large scale problems. Cooperative Coevolution framework is a popular approach to deal with large scale optimization. It is based on a divide-and-conquer manner. This paper proposes a novel algorithm called DISCC to tackle large-scale optimization problems. It adopts a function-based grouping decomposition strategy called Dependency Identification Grouping to distinguish the interactive variables in the decision space. The grouping strategy aims to find the most suitable arrangement for the variables in order to minimize the limitation that occurs when they are grouped into different groups. The experimental results show this new algorithm is more effective than the existing Cooperative-Coevolution-based algorithms.

Multiple Gravity Assist Spacecraft Trajectories Design Based on BFS and EP_DE Algorithm

The paper deals with the multiple gravity assist trajectories design. In order to improve the performance of the heuristic algorithms, such as differential evolution algorithm, in multiple gravity assist trajectories design optimization, a method combining BFS (breadth-first search) and EP_DE (differential evolution algorithm based on search space exploring and principal component analysis) is proposed. In this method, firstly find the possible multiple gravity assist planet sequences with pruning based BFS and use standard differential evolution algorithm to judge the possibility of all the possible trajectories. Then select the better ones from all the possible solutions. Finally, use EP_DE which will be introduced in this paper to find an optimal decision vector of spacecraft transfer time schedule (launch window and transfer duration) for each selected planet sequence. In this paper, several cases are presented to prove the efficiency of the method proposed.

Application of Latitude Stripe Division in Satellite Constellation Coverage to Ground

Grid point technique is a classical method in computing satellite constellation coverage to the ground regions. Aiming at improving the low computational efficiency of the conventional method, a method using latitude stripe division is proposed, which has high efficiency, and we name it latitude stripe method. After dividing the target region into several latitude stripes, the coverage status of each latitude stripe is computed by means of the spherical geometry relationship in the first orbital period. The longitude coverage intervals in the remaining orbital periods are computed by sliding the coverage status in the first orbital period. Based on this method, the instantaneous and cumulative coverage in simulation time can be calculated more efficiently. As well, the relationship between the cumulative coverage and altitude can be computed fast by this method, which could be used in the optimized design of repeating sun-synchronous orbits. The comparison between the conventional grid point method and the latitude stripe method shows that the latitude stripe method has high efficiency and accuracy. Through various case studies, the optimization in repeating sun-synchronous orbits design is successfully represented.

Asteroids exploration trajectory optimal design with differential evolution based on mixed coding

With the development of space technology, asteroid exploration will become a hotspot in the deep space exploration field. Space flight trajectory has the following requirements: needing a long time, having many engineering constraints, having a large number of targets, and having a series of feasible solutions. So how to find the global optimum flight program is the core issue of the deep space exploration trajectory design. This paper proposes a novel method to design the optimal trajectory by differential evolution (DE) algorithm for asteroid exploration based on mixed coding. In our method, the celestial sequence and the time sequence are coded together into the chromosomes of DE and optimized them simultaneously. The chromosomes are designed to include four parts: the celestial sequence, the exploration type, the time sequence, and the return time. The algorithm can make full use of the characteristics of the high efficiency and global optimization ability of differential evolution and can also avoid the problem of high complexity of the branch-and-bound algorithm and the problem of nonglobal optimal solution of the greedy algorithm. The algorithm is adopted to solve the Fourth Contest of National Space Orbit Design in China, and the result shows that both the computational efficiency and the performance of the algorithm are superior.

An improved differential evolution algorithm based on suboptimal solution mutation

In order to improve the drawbacks of DE algorithm with DE/best/1 such as the rapid convergence speed and local optimum, this paper proposes an improved DE algorithm. Based on the DE/best/1 mutation operator, a new mutation operator is constructed. The best M individuals are summed as a new individual to replace the base individual of the DE/best/1. This is helpful to avoid falling into local optimum for the fast convergence. Simulation experiments demonstrate that the proposed algorithm outperforms some standard DE variants.

Memetic Differential Evolution with an Improved Contraction Criterion

Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization. In this paper, we present an improved memetic differential evolution algorithm for solving global optimization problems. The proposed approach, called memetic DE (MDE), hybridizes differential evolution (DE) with a local search (LS) operator and periodic reinitialization to balance the exploration and exploitation. A new contraction criterion, which is based on the improved maximum distance in objective space, is proposed to decide when the local search starts. The proposed algorithm is compared with six well-known evolutionary algorithms on twenty-one benchmark functions, and the experimental results are analyzed with two kinds of nonparametric statistical tests. Moreover, sensitivity analyses for parameters in MDE are also made. Experimental results have demonstrated the competitive performance of the proposed method with respect to the six compared algorithms.

Global optimisation of multiple gravity assist spacecraft trajectories based on search space exploring and PCA

This paper deals with the design of optimal multiple gravity assist trajectories. An algorithm combining search space exploring and PCA (principal component analysis) is proposed. In this algorithm, firstly estimate the general position of function value valley by initializing a number of samples in global search space, and pick out a part of excellent samples from the initial samples. Then cluster the remaining excellent samples into several communities, researching on each community by PCA (principal component analysis), which can reflects the relationship between objective function and variables, as well as provide the densest direction of samples distribution. Count out distribution range of samples in each community which also represents the space size of it. Divide the communities in which we can find a better value when search with standard differential evolution algorithm into several small spaces from the direction we find. Finally, search the optimal value with standard differential evolution algorithm in every small space gained. Experimental results show that this method can effectively improve the optimization results.

EP_DE II: A significant algorithm to search the optimal solution for global optimization of multi-gravity assist trajectory

The paper deals with the design of the optimal multiple gravity assist trajectories. An improved search algorithm named EP_DE II with search space exploration, principal component analysis, guarantee mechanisms, and processing methods of search space is proposed based on EP_DE. First, a parameter is employed to start retaining the boundary information of the whole population. When the number of generations in evolutionary process reaches to this parameter, distribution range of population will be retained for the later computation. The best solutions in each generation are also recorded until the end of EP_DE II. Then the principal component analysis was conducted to find a cutting dimension for local search space, and selection process of cutting points are directed by the stored information before. Finally, search process is performed in all partitions of the search space. Global search experiments concerned about Benchmark Cassini1 and fly-by sequence EVVEEJS are presented to prove the efficiency of EP_DE II algorithm, comparing with basic differential evolution algorithm and EP_DE algorithm.