Efficient Satellite Scheduling Based on Improved Vector Evaluated Genetic Algorithm

Abstract

Satellite scheduling is a typical multi-peak, many-valley, nonlinear multi-objective optimization problem. How to effectively implement the satellite scheduling is a crucial research in space areas.This paper mainly discusses the performance of VEGA (Vector Evaluated Genetic Algorithm) based on the study of basic principles of VEGA algorithm, algorithm realization and test function, and then improves VEGA algorithm through introducing vector coding, new crossover and mutation operators, new methods to assign fitness and hold good individuals. As a result, the diversity and convergence of improved VEGA algorithm of improved VEGA algorithm have been significantly enhanced and will be applied to Earth-Mars orbit optimization. At the same time, this paper analyzes the results of the improved VEGA, whose results of performance analysis and evaluation show that although VEGA has a profound impact upon multi-objective evolutionary research,  multi-objective evolutionary algorithm on the basis of Pareto seems to be a more effective method to get the non-dominated solutions from the perspective of diversity and convergence of experimental result. Finally, based on Visual C + + integrated development environment, we have implemented improved vector evaluation algorithm in the satellite scheduling

A mixed integer linear programming model for multi-satellite scheduling

“…greedy approaches (Bianchessi & Righini, 2008;Wang et al, 2011) or local searches (Bonissone et al, 2006)) and metaheuristic algorithms (e.g. tabu searches (Habet et al, 2010;Vasquez & Hao, 2003;Bianchessi et al, 2007), genetic algorithms (Mao et al, 2012;Sun et al, 2010;Xhafa et al, 2012Xhafa et al, , 2013, evolutionary algorithms (Bonissone et al, 2006;Salman et al, 2015), and simulated annealing algorithms (Yao et al, 2010;Xhafa et al, 2013)). While these optimization techniques show improvements towards obtaining the optimal or near-optimal solutions, they typically require extensive parameter tuning and cannot provide quality guarantees for the obtained solutions.…”

“…Additionally, the swing angle and rotation angle of the resource must be set to point at the target. Thus, a setup time between two consecutive successful observations has to be considered to adjust the orientation of the resource (Mao et al, 2012). While the number of EOS is continuously increasing, so is the number of observation requests.…”

Task Scheduling of Agile Satellites with Transition Time and Stereoscopic Imaging Constraints

“…A typical Earth observation satellite has up to 10 opportunities to acquire the image of a target every day, while the number of requested missions can be hundreds [3][4][5]. This situation necessitates the selection and scheduling of imaging tasks to maximize the benefits obtainable by operating satellites [6][7][8][9][10][11][12].…”

An asynchronous MOPSO for multi-objective optimization problem

“…In the successive researches, non-dominating sorting genetic algorithms (NSGA, Srinvas and Deb 1994), multi-objective genetic algorithm (MOGA), vector evaluated genetic algorithm (VEGA) were designed with variants of GA, and have been applied to various MOO problems [1,2,4]. Similar to GA, PSO was also incorporated with Pareto dominance, algorithm known as multi-objective particle swarm optimization (MOP-SO) was proposed by Coello Coello (2002).…”