Research On Function Optimization And Cloud Task Scheduling Methods Based On Social Learning Optimization Algorithm

Swarm intelligence algorithm,as a branch of artificial intelligence,has been a hot topic in the field of computer science.The algorithm has become the main method to solve the problem of function optimization and discrete cloud task scheduling.In recent years,domestic and foreign scholars proposed a variety of swarm intelligence algorithms that simulate the cooperative behavior of different kinds of animals.However,due to the lack of learning ability of these algorithms,their performance in optimization problems is affected.It becomes a key scientific problem that how to improve the performance of swarm intelligence algorithm to solve these two kinds of problems becomes a key scientific problem.Social Learning Optimization Algorithm(SLO)is a novel swarm intelligence algorithm that simulates the evolution process of human social intelligence,which is in line with the natural law of the evolution of human intelligence.SLO algorithm has good optimization ability because the simulation object of SLO algorithm is the human society with the highest intelligence level,and the learning mechanism is introduced.At present,there has little research and application about SLO algorithm,in order to be more efficient to solve the function optimization and cloud task scheduling algorithm,this paper separately proposed two kinds of improved SLO algorithm for optimization problems.The algorithm based on Social Learning Optimization Paradigm,according to the characteristics of function optimization and cloud task scheduling problem,this paper designs the detailed optimal operation.Through the comparison with other algorithms,the proposed method has better global searching ability and convergence speed,can effectively improve the accuracy of the problem.The main research work in this paper has the following two points:1.Research on Social Learning Optimization algorithm for function optimization.Aiming at the problem of function optimization,based on the SLO paradigm,a social learning optimization algorithm for function optimization is proposed.In this algorithm we were designed evolutionary space operator in three layers separately that combined with the characteristics of function optimization problems;The inertia weight and learning retention factor are added into the observation learning operator to increase the population diversity and accelerate the convergence rate;The change of step control to reduce the interference of the individual to the excellent individual in imitation learning operator.Finally,the experimental results show that the SLO proposed in this paper has better performance in solving function optimization problems.2.Research on cloud task scheduling based on improved SLO algorithm.This paper established the mathematical model of the cloud task scheduling problem,through the analysis of the characteristics of the problem,proposed an improved Social learning optimization algorithm for cloud task scheduling problem.We introduced the SPV method for the solution space of the problem of mapping,and designed the operator of cloud task scheduling problem.Finally,verification and analysis the algorithm presented in this paper on CloudSim simulation platform,and compared with the existing methods,the improved SLO algorithm has a good effect in solving the problem.