Dynamic multi-objective evolutionary algorithm with center point prediction strategy using ensemble Kalman filter

Abstract

Dynamic multi-objective optimization problems are the multi-objective optimization problems in which the objectives change with environment and time, and the optimization algorithm for solving such problems must be able to track the changed pareto optimal set and further explore the real pareto optimal front. It is a difficult and hot topic in dynamic multi-objective evolutionary algorithm to accurately predict the direction of population movement after environmental changes. In this paper, the ensemble Kalman filter is introduced into dynamic multi-objective optimization problems to predict the population center point after environmental changes. ensemble Kalman filter is a four-dimensional assimilation method that uses Monte Carlo short-term ensemble prediction method to estimate the prediction error covariance and has achieved great success in many fields. In the proposed algorithm, the new population center point is predicted by the ensemble Kalman filter prediction model according to the historical population information when the environment changes, and then the population is reinitialized according to the predicted population center point. The experimental results show that the proposed algorithm is superior to other comparison strategies in most test instances.