使用新预测模型的动态多目标优化算法

A new dynamic multiobjective optimization algorithm is proposed to solve the problem that the existing dynamic multiobjective optimization algorithms have poor ability to track the rapid changing optimal solutions for practical applications, and the algorithm uses a new prediction model that combines the prediction value of central point and the vertical disturbance component. First, the central point of the optimal solution set before change is calculated as a prediction object, which changes the way that all solutions are usually used for prediction and improves the efficiency of the algorithm. Second, the history information of the algorithmic iterations is combined to select the location, velocity and acceleration as a state vector of prediction. The tracking ability for change in the solution set is ensured in most circumstances. At last, a hyperplane random disturbance that is perpendicular to the predicted direction of change is added to the predicted new solution to enhance the diversity of the knowledge set, and the convergence speed of the algorithm is improved. Experimental results show that the proposed algorithm is superior to the other 3 state-of-the-art dynamic multiobjective evolutionary algorithms in 75% test cases, and the run time of the algorithm is 39% lower than those of dynamic multiobjective optimization algorithms based on Kalman filter.