The Improvement Of Moth Flame Algorithm And Its Application

Moth flame optimization algorithm is a new metaheuristic optimization algorithm proposed by Seyedali Mirjalili in 2015.The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation,which solve the optimization problem by recording the best position and spiral search.Two subjects of research in moth flame optimization algorithm are to expanse the application field and to improve the optimization performance.Some novel methods are desiged to improve the performation of moth flame optimization algorithm with revolving the two subjects in the paper.In addition,the improved algorithms are used to solve some complex optimization problems.The main contents are summarized as follows:(1)Moth flame optimization algorithm based on dimension learning and quadratic interpolation was proposed and its application in data clustering.K-means clustering algorithm is one of the most commonly clustering methods based on partition.However,it highly depends on the initial solution and is easy to trap into the local optimum.Firstly,A moth flame optimization algorithm based on dimension learning and quadratic interpolation is proposed to improve the quality of solution and convergence speed of the basic algorithm.The Tent chaotic map is used to generate the initial population with better diversity to enhance the global searching ability of the algorithm.Dimension learning strategy for flame location generates better flame to guide the moth finding the optimal solution so that the searching efficiency of the algorithm is improved.The quadratic interpolation method is embedded into the basic algorithm to produce new moths,which is enhance the local search ability,so as to better balance the exploration and exploitation ability of the algorithm.The classical benchmark function is selected for numerical experiments and compared with the advanced metaheuristic algorithm.The experimental results and strategy effectiveness analysis show that the proposed algorithm has higher accuracy and stronger robustness than the compared algorithms.The high performance of the improved algorithm is then employed for optimize the location of cluster centers and is tested by using five datasets available from UCI machine learning laboratory.The experimental results show that the improved algorithm is suitable for solving k-means clustering problem,and good clustering results are obtained.(2)Hybrid moth flame optimization algorithm with learning strategy and neighborhood search was proposed and its application in job shop scheduling problem.Aiming at the job shop scheduling problem with the goal of minimizing the maximum completion time,a hybrid moth flame optimization algorithm with learning strategy and neighborhood search is first proposed to decrease the probability of local optima stagnation and improve the population diversity.The quasi-oppositional learning strategy is embedded into the flame updating process,which is helpful for the flame to jump out of the local optimal and have a higher chance to be closer to the unknown optimal solution.Then,moths are divided into two subgroup according to their fitness values.One of the subgroups is updated by ranking paired learning strategie to realize the information exchange between individuals,and another subgroup is updated by neighboring search strategy to increase the diversity of the population.This kind of parallel computing can improve the quality of the whole population more quickly.The classical benchmark functions and the CEC2017 benchmark functions are selected for numerical experiments,and compared with the advanced metaheuristic algorithms and improved algorithms respectively.The experimental results and statistical analysis show that the proposed algorithm has higher accuracy and stronger robustness.Finally,the proposed algorithm is applied to solve the benchmark instances in OR-Library,the experimental results verify that the proposed algorithm is effective while solving the job shop scheduling problem.