Abstract: The optimization problem is one of the common problems in the engineering field,the essence of most engineering problems is the function optimization problem.Pyramid evolution strategy(PES) algorithm can effectively set a balance between “exploitation” and “exploration” as well as “competition” and “cooperation” when solving function optimization problems,but there are still some shortcomings,such as slow convergence speed,low accuracy,and easy to fall into a local optimal.In order to solve these shortcomings,this paper proposes a pyramid evolution strategy based on dynamic nearest neighbor lasso(DNLPES).The DNLPES algorithm adaptively controls the selection range parameters of the target individual group based on the evolution.At the same time,the Euclidean distance is used to measure the difference between individuals in the target individual group.The difference information between individuals is used to guide the cooperation between individuals,the population evolution is completed by continuously generating new individuals and eliminating the individuals with poor fitness value.The DNLPES algorithm improves the accuracy of the algorithm by making full use of the difference information between individuals in the population and enhancing the cooperation between individuals.Comparing the DNLPES algorithm and the 7 algorithms on 9 test functions,experimental result shows that the DNLPES algorithm has a certain competitiveness in solving accuracy.Compared with the stan-dard PES algorithm,the DNLPES algorithm has obvious advantages in solving accuracy and convergence speed.

Key words: Dynamic neighbor lasso(DNL), Euclidean distance, Function optimization problem, Intelligent algorithm, Pyramid evolution strategy(PES)