Many multi-objective optimization problems in reality are dynamic, requiring the optimization algorithm to quickly track the moving optima after the environment changes. Therefore, response strategies are often used in dynamic multi-objective algorithms to find Pareto optimal. In this paper, we propose a hybrid prediction strategy based on the classification of decision variables, which consists of three steps. After detecting the environment change, the first step is to analyze the influence of each decision variable on individual convergence and distribution in the new environment. The second step is to adopt different prediction methods for different decision variables. Finally, adaptive selection is applied to the solution set generated in the first and second steps, and solutions with good convergence and diversity are selected to make the initial population more adaptable to the new environment. The prediction strategy can help the solution set converge while maintaining its diversity. The experimental results and performance show that the proposed algorithm is capable of significantly improving the dynamic optimization performance compared with five state-of-the-art evolutionary algorithms.

现实中，许多多目标优化问题都是动态的，需要优化算法在环境变化后快速跟踪运动的最优值。因此，在动态多目标算法中经常使用响应策略来找到帕累托最优。在本文中，我们提出了一种基于决策变量分类的混合预测策略，该策略包括三个步骤。在检测到环境变化之后，第一步是分析每个决策变量对新环境中个体收敛和分布的影响。第二步是对不同的决策变量采用不同的预测方法。最后，将自适应选择应用于第一步和第二步中生成的解集，选择具有良好收敛性和多样性的解决方案，以使初始人口更适应新环境。预测策略可以帮助解决方案集收敛，同时保持其多样性。实验结果和性能表明，与五种最新的进化算法相比，该算法能够显着提高动态优化性能。