Dynamic multi⁃objective optimization algorithm based on Kalman filter prediction strategy

In order to deal with the environmental changes of dynamic multi-objective optimization more effectively, a dynamic multi-objective optimization algorithm based on Kalman filter prediction strategy is proposed. In the evolution process, a new calculation method is used to calculate the population center point. When the environment changes, the Kalman filter prediction model is used to predict the current population center point, and the approximate true Pareto center point of optimal solution set is used to correct the prediction value, and new individuals are generated based on the modified center point to reinitialize the population during the running of the algorithm; In order to increase the diversity of the population, five new individuals are randomly generated from the search space during the operation of the algorithm, and the corresponding number of individuals in the current population are randomly replaced. Compared with other dynamic multi-objective optimization algorithms in multiple test functions, the results show that the value of Modified Inverted Generational Distance (MIGD) in the whole evolution process is relatively small, and the value of Inverted Generational Distance (IGD) in the evolution is generally smaller than that of the contrast algorithm, and the calculation time is equivalent to that of the comparison algorithm. © 2022, Jilin University Press. All right reserved.