Improved Research And Application Of Moth-Flame Optimization

The swarm intelligence optimization algorithm is an important branch of intelligent optimization algorithm.Because of its advanced performance in dealing with non-linear,complexity and other problems,it has been studied by many scholars.The moth fire suppression algorithm(MFO)is a new type of intelligent optimization algorithm developed in recent years.Due to its simple structure and strong search performance,it has attracted widespread attention.However,the moth fire suppression algorithm also has some shortcomings,and it is in engineering.The field of application is in its infancy.To this end,this paper starts with the algorithm,makes necessary improvements to the standard moth fluttering algorithm,and then tests and verifies it.The performance of the algorithm is verified through the application of array antenna synthesis and microstrip antenna design optimization.The main researches in this article are:1.The principle and structural model of the standard moth fire suppression algorithm are analyzed in detail,and the existing improvement strategies and defects are studied to provide a basis for subsequent work.2.Aiming at the shortcomings of the current moth fire suppression algorithm,an improved moth fire suppression algorithm(MSMFO)is proposed.The main improvements in this paper are: First,the uniform design idea is adopted,and the good point set theory is applied.Initialization of the population;Then,an out-of-bounds reset strategy is introduced into the algorithm to intervene on individuals who exceed the solution space;subsequently,an improved gravitational search algorithm is integrated into the iterative mechanism of the moth flutter algorithm,and a new The dynamic gravity coefficient increases the optimization performance of the algorithm.Finally,in order to avoid the algorithm falling into a local optimum,Cauchy mutation is performed on the individual to improve the global search ability of the algorithm.Finally,the algorithm is tested with test functions,which verifies the reliability of the algorithm.3.Apply the improved moth flutter algorithm to the array antenna pattern synthesis,and verify the reliability of the MSMFO algorithm on the array antenna synthesis problem through the comprehensive design of low sidelobe,deep zero and deep zero segment pattern design..4.In order to solve the problem of simulation time consuming,the MSMFO algorithm in this paper is combined with the agent model based on BP neural network,and it is used to optimize the design of slotted microstrip antenna and WLAN dual-band antenna experiments.It has more advantages in speed and effectively guarantees the accuracy of the experiment.