基于参考点预测的动态多目标优化算法

为了快速跟踪动态多目标优化问题变化的Pareto前沿，本文提出一种基于参考点预测策略的动态多目标优化算法（PDMOP）.该算法对关联到相同参考点的个体建立时间序列，并对这些时间序列通过线性回归模型预测新环境下种群.同时，将历史时刻的预测误差反馈到当前预测中来提高预测的准确性，并在每个预测的个体上加入扰动来增加初始种群多样性，从而能够加快算法在新环境下的收敛速度.通过4个标准测试函数对该算法测试，并和两个现有算法对比分析，结果表明所提算法在处理动态多目标优化问题时能够保持良好的性能.

Dynamic Multi-objective Optimization Algorithm Based on Reference Point Prediction

In tracking the moving Pareto front of dynamic multi-objective optimization problem as soon as possible, a new algorithm based on reference point prediction (PDMOP) is proposed. Firstly, PDMOP distributes the past individuals to different time series according to the information of reference point association. Then for these time series, a linear regression model is used to predict the new environment population. At the same time, historical prediction error is added to the current prediction to enhance prediction accuracy, and a Gauss noise is added to every new individual to increase the initialized population diversity. In this way, the algorithm can speed up convergence in the new environment. The results of four benchmark problems and the comparison with other two existing dynamic multi-objective algorithms indicate that the proposed algorithm can maintain better performance in dealing with dynamic multi-objective problems.