Multi-strategy Sparrow Search Optimization Algorithm And Its Application And Simulation

The sparrow search algorithm(SSA)is a novel swarm intelligence optimization technology designed and proposed by the Chinese in 2020.It has the advantages of a few adjustment parameters,a clear model structure,and easy implementation.Now it has been successfully applied to research in many fields.With the deepening of utilization,SSA correspondingly exposes some problems,such as insufficient convergence accuracy,weak global exploration ability,and easily falling into local extremum,which need to be solved by scholars.Hence,based on the analysis of SSA performance,this paper introduces a variety of evolution strategies to correct the existing defects of the algorithm and applies the improved algorithm to solve practical problems,to achieve the purpose of improving the optimization performance of SSA and expanding its application field.The main work of this paper is as follows:(1)To improve the node deployment coverage of the wireless sensor network(WSN),a novel enhanced sparrow search algorithm(NESSA)is proposed as a processing tool to realize this optimization problem.The improved content of NESSA covers three aspects: population initialization,iterative search,mutation disturbance,and correction of the defects for SSA from multiple perspectives.Moreover,a universal WSN node coverage optimization model of the swarm intelligence algorithm is built,and the performance of six algorithms to solve this problem is further explored with the coverage rate as the evaluation index.The simulation test results show that NESSA can obtain a better node deployment scheme in the same environment,and the coverage rate is significantly improved compared with SSA.(2)To improve the application ability of SSA in function optimization and equations solution,an improved sparrow search algorithm(ISSA)is proposed,which uses a good-point set to initialize the population and introduces the t-distribution random number mutation disturbance in the iteration.To evaluate the performance of the proposed ISSA,based on 23 sets of benchmark functions,linear equations,nonlinear equations,and trigonometric function transcendental equations,a comparative optimization experiment including five algorithms are designed.The simulation test results show that compared with the original algorithm,ISSA has achieved significant improvement in solving accuracy,convergence speed,and iteration stability,and the optimization results are more competitive than other algorithms.(3)The improved sparrow search algorithm(ISSA)is further applied to solve general dynamic optimization problems in the chemical industry,and corresponding optimization models are constructed using Control Variable Parameterization(CVP)method.Three typical examples are then solved.The simulation test results indicate that ISSA has the feasibility and effectiveness to solve this problem.