Research And Application Of Multi-strategy Impeoved Sparrow Search Algorithm Integrating Symbiosis Relationship

The sparrow search algorithm is a new type of swarm intelligence algorithm proposed by Xue Jiankai and others which is based on the biological behavior of sparrow populations.This algorithm has advantages such as simple structure and fast convergence speed,but it also has defects such as being prone to falling into local optima and blindly updating positions.Starting from the basic sparrow search algorithm,this thesis integrates two optimization methods to improve algorithm performance,and uses test functions and actual application to verify the performance advantages of improved algorithms.The primary topics covered in this research are outlined below:(1)Aiming at the problems of the sparrow search algorithm,such as poor initial population,weak global search ability and insufficient convergence accuracy,a multi-strategy improvement method is proposed.First,the population is processed by Circle mapping,and the dynamic proportion is introduced to adjust the proportion of species,improve stability,and balance the global search and local search capabilities;Then,use Levy flight to promote the location update method of the discoverer,and the location update method of the watcher is adjusted at the same time to strengthen the global search ability;Then perform mutation on the current global optimal position to improve convergence accuracy.Validate algorithm performance through benchmark testing functions and WSN coverage optimization.(2)In view of the problems of population quality reduction and the troubles of jumping out of the local optimal solution in the later stage of multi-strategy improved methods,this paper aims to develop the performance of the algorithm on the basis of multi-strategy improvement combined with algorithm fusion optimization.Firstly,symbiosis was introduced in the late iteration period of the improved algorithm,and the generation method of new individuals and the boundary processing method of the algorithm were improved.By virtue of the mutation ability of the fusion algorithm,the ability to break through the local extreme value and the convergence accuracy were improved.Finally,the benchmark function was selected for comparative experiments and applied to the optimization problem of pressure vessel design,verifying the effectiveness of the above improvement strategies in improving algorithm performance.(3)Apply the multi-strategy improved sparrow search algorithm that integrates symbiotic relationships to optimize cold chain delivery paths,solving practical problems.By reasonably planning the delivery path and comparing it with other optimization algorithms for solving this scenario.The simulation experimental data shows that the improved algorithm is feasible and practical in practical problems.