Improvement And Application Research Of Moth-flame Optimization Algorithm

As a new swarm intelligence optimization algorithm,moth-flame optimization algorithm has the advantages of fast convergence speed,simple structure and strong robustness,which is one of the hot spots of scholars.However,with the deepening of research,it is found that the moth-flame optimization algorithm is easy to fall into local optimum and the optimization accuracy is not high.In this paper,some improvements are made to the shortcomings of the moth-flame optimization algorithm,and the improved algorithm is applied to some classical optimization problems.The purpose is to improve the performance of the moth-flame optimization algorithm,improve the theoretical basis of the algorithm,and expand its application range.The main results of this paper have the following three aspects:(1)Aiming at the problem that the moth-flame optimization algorithm has low optimization accuracy and is easy to fall into local optimal solution,the pheromone selection strategy of black widow algorithm is introduced,and a moth-flame optimization algorithm based on pheromone selection strategy is proposed.The algorithm makes full use of the moth population resources through the pheromone selection strategy,thereby improving the optimization accuracy.The algorithm is applied to solve practical engineering optimization problems.The experimental results show that the algorithm has good performance and can search efficiently.(2)Aiming at the problem that the moth-flame optimization algorithm has a slow convergence speed when solving slightly complex or large-scale functions,a moth-flame optimization algorithm with dynamic adjustment flight strategy is proposed.Based on the original algorithm,the algorithm adds a linear flight strategy and a scaling factor that fuses the gray wolf algorithm mechanism to achieve the goal of improving the global optimization ability and convergence rate.The algorithm is used to guide the clustering center of the K-means clustering algorithm.The experimental results show that the algorithm can improve the clustering accuracy and is a very effective and practical method.(3)Aiming at the problems of long search time and low search efficiency in moth-flame optimization algorithm,a multi-strategy fusion moth-flame optimization algorithm is proposed.The algorithm uses chaotic mapping to optimize the initial population of the algorithm,introduces sine cosine operator and novel flip bucket foraging strategy to promote the transmission of optimal moth individual information in the population,and improves the ability of the algorithm to jump out of local optimum.Finally,it is used to optimize the design of power system stabilizer,which verifies the effectiveness and practicability of the algorithm.