Research On Web Optimization Application Based On Grey Wolf Algorithm

In today's era,we must not leave two things,one is oxygen,the other is Internet.The convenience brought by all kinds of new Web applications is self-evident.However,on the one hand,the increasing user traffic has brought unprecedented pressure to the corresponding Internet enterprises.Especially for small and medium enterprises,the budget expenditure of hardware costs is limited.How to make use of limited resources to make the performance of Web applications the most Big promotion is a very practical problem,and a Web application can be deployed with many parameters.It is an inevitable trend to take automatic parameter tuning instead of manual parameter tuning.On the other hand,users' changing needs and experience needs force Internet companies to develop new business areas and develop new Web applications.As a new type of Web application mode,Web service has developed rapidly in recent years.How to combine the existing Web services into a new Web service which meets the requirements of the user's service quality has become a new application demand and research hotspot.In this paper,a grey wolf optimization algorithm based on hybrid improvement strategy is applied to solve the Web parameter optimization problem.It is proved by experiments that the improved grey wolf optimization algorithm can improve convergence speed and get a better solution.After that,the improved grey wolf optimization algorithm is applied to the Web parameter optimization problem.The experiment shows that the algorithm can make the Web system get better performance and have practical application value.For the problem of Web service composition optimization,first of all,the multi-objective grey wolf optimization algorithm combined with the dual document strategy and the crossover and mutation operation in the genetic algorithm,making the improved multi-objective grey wolf optimization algorithm show good convergence and diversity,and apply the algorithm to the Web service combination optimization problem.Experiments show that the algorithm is effective and has good performance,which is of great significance for practical work.