A Research On Multi-Strategy Differential Evolution Algorithm And Its Application In The Multi-Satellite Cooperative Task Planning

In this paper,a multi-strategy differential evolution algorithm is designed from three aspects of policy choice,combination mechanism and parameter self-adaptive mechanism,and the proposed algorithm is tested on the standard test problem and the application problem,and the problem of multi-satellite cooperative scheduling is solved by using the designed algorithm.In the algorithm design,a variety of group mechanisms are used to combine the multiple performance complementary mutation strategies with the effective parameter adaptation mechanism.The problem of multi-star task planning is a kind of multi-constrained optimization problem,and two different constraint processing methods are used to balance the constraint conditions and the objective function.After the clustering condition and the method are set,the cooperative planning model of the satellite based on the task clustering is established to improve the observation efficiency,and the combination optimization characteristic of the problem is treated by using the probability evaluation operator.The main work and innovation points of the paper are as follows:A variety of group mechanisms are used to combine multiple performance complementary differential algorithm variants or strategies to take into account diversity and convergence.When combining a plurality of different algorithm variants,three algorithms are used in the early phase of the algorithm to perform the cooperative evolution,the diversity of the algorithm is maintained,the convergence speed is improved by using the method of reducing the population size in the later stage,and the balance between the sub-populations is maintained by using the elite strategy.When combining multiple variant strategies,the diversity of the algorithm is enhanced by using three improved variant strategies,respectively.The test results show that the proposed algorithm has better results than other advanced differential evolution algorithms.the multi-strategy differential evolution algorithm is applied to solve the attitude optimization problem of space power station.using a simpler and more effective combination mechanism,two constraint processing techniques are introduced: feasibility rule and ?-constrained method,which are used for comparison between similar individuals and between parents and offspring,respectively,to balance constraints and objective functions.The experimental results show that the constrained multi-strategy differential evolution algorithm has good application ability in constrained optimization problems.According to certain clustering rules,multiple adjacent tasks are combined into one clustering task.When the satellite is in transit,the clustering task is completed at one time,the attitude adjustment time and energy consumption are reduced,and the observation efficiency is improved.A multi-satellite cooperative observation scheduling model based on task clustering is established.According to the constraints,the improved multi-strategy differential evolution algorithm is used to solve the problem,the strategy of overlapping performance in the algorithm is removed,and the probability evaluation operator is used for discrete transformation.The simulation results on the CSTK platform of our research group show the effectiveness of the proposed algorithm.To sum up,this paper makes an in-depth study of the multi-strategy differential evolution algorithm from two aspects of the algorithm and the application.In this paper,a multi-strategy differential evolution algorithm with excellent performance is proposed.In the application,on the basis of the previous algorithm,a multi-strategy differential evolution algorithm with corresponding constraints is proposed for the characteristics of different optimization problems.The experimental results show the effectiveness of the proposed algorithm.