Research On Multi-group Co-evolution Differential Algorithm With Mutation Mechanism And Its Application

With the development of the social economy,human life and work have been related to intelligence closely.As an important branch of"artificial intelligence",the development and application of intelligent optimization algorithms promote the development of human society.Among them,the intelligent optimization algorithm combined with the path planning problem,widely used in navigation,drones and intelligent transportation,has become one of the important development directions of,smart manufacturing".As an important method in intelligent optimization,differential evolution algorithm is widely used.This paper is based on the differential evolution algorithm and path planning.The related simulation and simulation experiments are carried out using Matlab software.The paper can be summarized as the following two aspects:(1)Proposal of a multi-group co-evolutionary differential algorithm with a mutation mechanism(abbreviated as:pDEPSO algorithm).Aiming at the problem that traditional differential algorithm is easy to fall into local optimum,this paper proposes to optimize it by combining co-evolution mechanism and pDE mutation operator.Firstly,a co-evolution mechanism is added to the algorithm,and k-means clustering is used to realize multiple grouping.Then,in each sub-population,a pDE mutation operator based on the sorting mechanism is constructed.Finally,the information exchange of multiple group algorithms is realized through intra-species and inter-species learning mechanisms.The effectiveness of the improved algorithm is verified by experiments.It is concluded that the improved algorithm pDEPSO algorithm can speed up the convergence and avoid late fall into local optimum.(2)Study the application of pDEPSO algorithm in robot path planning.The pDEPSO algorithm is applied to the robot path planning problem to verify the performance of the algorithm in static and dynamic environments.The experiment proves that the pDEPSO algorithm can avoid obstacles in real time and find the optimal or sub-optimal path,effectively save walking time and further verify the proposed algorithm.Therefore,it can be further verified that the proposed algorithm is an efficient and stable intelligent algorithm.