Real-world multiobjective optimization problems usually involve conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, evolutionary algorithms based on prediction models have been considered promising. However, most existing approaches only make predictions based on the linear correlation between a finite number of optimal solutions in two or three previous environments. These incomplete information extraction strategies may lead to low prediction accuracy in some instances. In this paper, a novel prediction algorithm based on incremental support vector machine (ISVM) is proposed, called ISVM-DMOEA. We treat the solving of dynamic multiobjective optimization problems (DMOPs) as an online learning process, using the continuously obtained optimal solution to update an incremental support vector machine without discarding the solution information at earlier time. ISVM is then used to filter random solutions and generate an initial population for the next moment. To overcome the obstacle of insufficient training samples, a synthetic minority oversampling strategy is implemented before the training of ISVM. The advantage of this approach is that the nonlinear correlation between solutions can be explored online by ISVM, and the information contained in all historical optimal solutions can be exploited to a greater extent. The experimental results and comparison with chosen state-of-the-art algorithms demonstrate that the proposed algorithm can effectively tackle dynamic multiobjective optimization problems.

翻译：现实世界的多重目标优化问题通常涉及随时间而变化的相互冲突的目标,这需要优化算法,以便在环境变化时快速跟踪Pareto最佳前台(POF),近年来,基于预测模型的进化算法被认为是有希望的;然而,大多数现有方法仅根据前两个或三个环境中数量有限的最佳解决方案之间的线性关联作出预测。这些不完整的信息提取战略可能会导致某些情况下的预测准确性低。在本文件中,基于递增支持矢量机(ISVM-DMOEA)提出了一个新的预测算法。我们把动态多目标优化问题(DMOP)的解决作为一个在线学习过程。我们利用持续获得的最佳解决方案更新递增支持矢量机而不在早期丢弃解决方案信息。之后,ISVM被用于过滤随机解决方案,并在下一个时刻生成初始人口。为克服培训样本不足的障碍,在ISVM培训之前,将实施合成少数群体过量的抽样战略。这一方法的优势在于,可以在线探索动态多目标解决方案(DOPM)之间的非线性关联性关联性关系,使用不断获得ISVM的优化分析结果。