A Differential Evolution Method for Agile Satellite Multi-objective Mission Planning

Abstract

The method includes: converting a solution set space formed by a decision variable into a population; initializing the population and algorithm parameters of the population; generating a donation vector, a trial vector, and a subgeneration according to the algorithm parameters of the population; adding the subgeneration into the population to obtain a mutational population , and decoding an individual to obtain adaptability; determining whether a current quantity of iterations i is less than a total quantity of iterations; if so, selecting an individual in the mutational population, generating a new population, and updating algorithm parameters of a corresponding population; otherwise, removing a dominated solution in an elite solution set, and adding a non-dominated solution in the mutational population into solutions that are not dominated by an individual in the elite solution set, so as to update the elite solution set; and step 8: sorting individuals in the elite solution set, and outputting a specified quantity of individuals according to an obtained sorting sequence.

Description

technical field [0001] The invention relates to a differential evolution method for agile satellite multi-objective task planning. Background technique [0002] Agile satellite multi-objective programming problems have obvious time-dependent characteristics, and analyzing the research status of time-dependent scheduling methods is of great help to the research of agile satellite multi-objective programming. The current research on time-dependent scheduling methods mainly focuses on three aspects: time-dependent processing time, time-dependent revenue, and time-dependent switching time. Gupta and Browne first proposed time-dependent processing time. Subsequent scholars studied the extended form of the problem. The objective function of the research is mainly divided into minimizing the sum of the completion time and minimizing the processing cycle, and the dependence of processing time on the processing start time. The relationship is mainly linear, and scholars such as Huan...

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Problems solved by technology

When facing the time-dependent conversion time problem, Pralet uses a time-dependent simple time network to deal with it, corresponding to the use of constraint propagation in agile satellites to adjust the start time of the next imaging task, but does not consider the optimization of the problem Model and Algorithm

Chen Cheng studied the workpiece processing problem with time-dependent conversion time and time-dependent income, designed a hybrid difference algorithm, and applied it to agile satellite imaging task planning, which has a good solution effect, but the defect is that the considered target is only Single target planning for point target imaging cannot be applied to multi-target situations

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