Research And Application Based On Two Guidance Mechanizms Grey Wolf Optimizer

The traditional grey wolf optimizer(GWO)has the advantages of fewer parameters,easy implementation and fast convergence,but it also has some disadvantages,such as easy to fall into local optimal solutions and low convergence accuracy,which restrict its application in engineering field.Therefore,this paper proposes a two guidance mechanisms grey wolf optimizer,which mainly includes:1.A two guidance mechanisms grey wolf optimizer is proposed.Firstly,inspired by the particle swarm optimization,a new hunting model was proposed to improve the search ability of the grey wolf optimizer by introducing the individual’s historical experience into the location updating method.Secondly,in order to avoid the problem that only the best three optimal individuals guide the position update of the population so that it is easy to fall into the local optimal solution,a sparse point guide operator is proposed to improve the diversity of the population,enhance the global exploration ability of the algorithm and the ability to jump out of the local optimal solution.Then,based on the idea of differential evolution and its improved types,a biased differential mutation operator is proposed to make full use of location information between individual positions to enhance the exploitation ability of the algorithm for complex local space and improve the accuracy of the algorithm.Finally,in order to balance the transition between exploration ability and exploitation ability of the algorithm,the roulette selection method is used to balance the two stages,and the algorithm still has the ability to jump out of the local optimal solution in the latter half stage.2.The feasibility of the proposed algorithm is verified on the complex optimization problems of IEEE CEC 2014,classical engineering design problems,circular antenna array design problem of communication system and economic load dispatch problem of power system,and the results show that the proposed algorithm is superior to other improved types of grey wolf optimizer in recent years.