Appliction Research Of Seagull Optimization Algoritjm

Seagull Optimization Algorithm(SOA)is a novel swarm intelligence technique which simulated the migration and attack behavior of seagulls.It has few parameters,easy operation,strong search ability,and simple mathematical model.However,the research of SOA is still in the early period and the SOA has demerits with regard to easily get trapped into local optimum,low precision and parameter sensitivity.Therefore,the shortcomings of the seagull optimization algorithm are analyzed and improved,and this paper proposed three different strategies for the seagull optimization algorithm.At the same time,the improved algorithm is applied to solve practical problems which goal is to expand the application field of the algorithm and further improve the theoretical basis of SOA.The main research work of this paper is as follows:(1)Considering the problem of seagull optimization algorithm that is easy to fall into local optimum,a seagull optimization algorithm with memory function(MSOA)is proposed.Based on the analysis of the respective characteristics of the seagull optimization algorithm and the memory function,the memory function is effectively embedded in the seagull optimization algorithm to improve the optimization ability of the algorithm.Firstly,the standard test function is used to verify that MSOA has good optimization performance.Secondly,the MSOA is further applied to solve equations.The experimental results showed that the algorithm has advantages in both the quantity and quality of solutions.(2)In order to solve the problem of SOA search ability,an adaptive weight strategy is introduced to balance the global search and local search capabilities of SOA,and the cosine strategy of inertia weight is used to enhance the local search ability of algorithm.The standard test function and the second-order temperature control delay system are used for testing.Through the comparison between algorithms,the result shows that the improved SOA has a better search performance.(3)The solution of the chemical dynamic optimization problem has important theoretical and practical significance,but it is difficult to find the optimal solution.Therefore,a hybrid seagull optimization algorithm(HSOA)is proposed to solve this problem.Firstly,introduce the cognitive part in the process of the seagull group's attack behavior to prevent the algorithm from falling into the local optimum.Secondly,the algorithm adds mechanism of natural selection,the fitness value is used to sort the population,and the best half is used to replace the worst half,so as to find out the optimal solution.Finally,the improved algorithm is applied to three classic chemical engineering problems.Through experimental simulation,the HSOA is better than the reference solution to a certain extent,which proves that the HSOA has better optimization ability.