Research On Improved Firefly Algorithm And Its Application

Swarm intelligence optimization algorithms belong to stochastic optimization algorithm by simulating the behavior of biological swarm intelligence,which have good search capabilities optimization abilities,and easy to implement,which are flexible and practical,less parameters and simple process.However,there are many discrete combination optimization problems that need to be solved in practical application,so it is urgent to improve the traditional FA.The main work of this article is as follows:1.Firefly algorithm based on the inertia weight logarithmic decreasingIn the light of the problems of slow convergence rate and falling into the local optimization easily for the firefly algorithm,the algorithm which based on the inertia weight logarithmic decreasing is proposed.Firstly,experiment shows the influence of inertia weight on population diversity of firefly algorithm,and then the logarithmic adjustment factor is introduced.Changes of logarithmic adjustment factor ensure the success rate.Finally,the simulation experiments for four kinds of typical function in the given number of iterations and precision.The test results show that the strategy can improve the algorithm's global convergence and convergence speed easily.2.Solving Traveling Salesman Problem Based on Improved Firefly AlgorithmIn view of traveling salesman problem(TSP)is an oldest combinatorial optimization problem,Firefly Algorithm(FA)shows great performance to complicated function optimization.Hence,in this paper,we used FA to solve TSP.Firstly,after the characteristics of TSP is analyzed,then the method of integer encoding is adopted to set the position of fireflies.Then,the logarithmic adjustment factor is introduced on the standard of firefly algorithm.Meanwhile,we combine with crossover,mutation and reverse operation in genetic algorithm(GA)to improve the population for each iteration of the population diversity and search ability,and it is applied to solve TSP.Finally,the numerical experiments shows that the proposed algorithm has faster convergence speed and optimization effectively.3.An Improved Firefly Algorithm and Its Application in Solving k-means clustering algorithmFor the problems of easily relapsing into local optimum in Firefly Algorithm(FA),an improved Firefly Algorithm is proposed.By introducing random weights and Levy flight strategy in cuckoo algorithm,we can enhance the diversity of population,so that both the global and local search ability of the algorithm balanced.The improved algorithm is used for k-means algorithm.We conducted experiments test on several groups of UCI data,the results show that the proposed algorithm has a higher accuracy and a better optimization ability.At the same time,the improved firefly algorithm solves the problem that k-means clustering algorithm is sensitive to initial values and is affected by abnormal data to some extent,which proves the feasibility of the improved algorithm.