Research On Multi-strategy Improved Grey Wolf Optimization Algorithm And Its Application

The grey wolf optimization algorithm is a new type of swarm intelligent optimization algorithm proposed to simulate the predation behavior of grey wolves in nature,which seeks the optimal solution by simulating the grey wolf to find prey,surround prey,and hunt.The algorithm has the advantages of simple structure,strong optimization ability,and easy implementation.Since the proposed algorithm,it has received extensive attention from scholars at home and abroad,but with the deepening of research,it has been found that the grey wolf optimization algorithm has shortcomings such as low search efficiency in the later stage and easy to fall into precocity.This paper analyzes and improves the shortcomings of the grey wolf optimization algorithm,aiming to improve the overall performance of the grey wolf optimization algorithm,and applies the improved algorithm to some complex engineering optimization problems,in order to improve the performance of the algorithm and broaden the application field of the algorithm.The main contents of this article are as follows:(1)Aiming at the problem that the search ability is not high due to the late aggregation of grey wolf population,a lion group algorithm is introduced,and a grey wolf optimization algorithm based on mixed lion group is proposed.The improved algorithm improves population diversity,expands the population search range,and applies the improved algorithm to function optimization and path planning problems.Experimental results show that the improved algorithm obtains ideal results in function optimization and path planning problems.(2)Aiming at the imbalance between global and local search ability of grey wolf population,an elite reverse strategy and adaptive weight strategy are introduced,and a mix-grey wolf optimization algorithm is proposed.In the early stage of search,the population is evenly distributed,and the introduction of adaptive weights in the later stage can better balance the global and local search capabilities of grey wolves,and is applied to the vehicle scheduling problem in logistics and distribution.Experimental results show that the improved algorithm can effectively save the total cost of logistics and distribution vehicle scheduling.(3)Aiming at the problem that the convergence speed of the grey wolf optimization algorithm is slow,the differential evolution algorithm is introduced into the grey wolf optimization algorithm,and a hybrid differential evolution-grey wolf optimization algorithm is proposed.Variation is used to select better individuals for optimization and apply them to optimal power flow calculation.Experimental results show that the improved algorithm provides an effective solution to the optimal power flow calculation problem.