To solve the problem that the global search ability and local search ability of traditional multi-objective evolutionary algorithm cannot be effectively balanced when solving the Pareto solution set

an adaptive cellular differential evolutionary algorithm based on multi-neighborhood structure is proposed. Based on the characteristics of the traditional cellular differential evolutionary algorithm

the improved algorithm uses a richer multi-neighbor structure to replace the original single neighbor structure

and the neighbor structure is adjusted reasonably according to the performance of the corresponding individual. At the same time

in the face of the complex requirements in the whole evolution process

the algorithm defines a mutation strategy with periodic variation to realize the adaptive adjustment in different evolution stages. Finally

the DTLZ series of test functions are used to test the performance of the algorithm. Compared with four classical multi-objective optimization algorithms

it is proved that the improved algorithm has better convergence performance and diversity of solution set.