Moth-flame Optimization Algorithm And Its Application Research

Moth-flame optimization(MFO)algorithm is a novel swarm intelligent optimization algorithms proposed by Mirjalili.The algorithm simulates the flight patterns of moths in nature.It has the advantages of simple structure,less adjustable parameters,easy implementation and strong robustness.Therefore,MFO algorithm has been widely concerned and studied by domestic and foreign scholars since it was proposed.It has been successfully applied to solve complex optimization problems.Moreover,MFO algorithm also has some disadvantages,such as easy to fall into the local optimal,excellent precision is not high.All of those limits the application of MFO algorithm.In this paper,some improvements are made to the shortcomings of MFO algorithm.Improved algorithms were applied to some classical optimization problems.The purpose of this paper is to improve the performance,theoretical basis and extension of the application of MFO algorithm.The main achievements of this paper are as follows:(1)Aiming at the shortcoming of slow convergence rate and low precision.On MFO algorithm,the Lévy flight strategy is added,MFO algorithm(LMFO)based on Lévy flight is proposed.The improved algorithm overcomes the weakness of MFO and enhances the global search ability of the algorithm,and effectively prevents the algorithm from falling into the local optimal stagnation.Compared to the other algorithms,LMFO has the potential to provide better performance.(2)The LMFO algorithm based on Lévy flight is applied to solve two practical engineering optimization problems,a new search area is established in the optimal individual neighborhood.In this way,the algorithm can perform a large number of effective search in the feasible domain.The robustness and efficiency of the algorithm is enhanced and the application range of the algorithm is widened.(3)In the paper,the simplex method is introduced in the algorithm,and SMMFO based on simplex method is proposed.SMMFO algorithm overcomes the defects that MFO easy to fall into the local optimal,increases the population diversity of the algorithm,strengthens its local search ability.It also improves the execution efficiency of the algorithm,accelerates the convergence rate of the algorithm,improves the performance of MFO algorithm for clustering analysis of data set.