IEEE Trans Cybern. 2022 Jul;52(7):6119-6130. doi: 10.1109/TCYB.2021.3059252. Epub 2022 Jul 4.
Dynamic multiobjective optimization problems are challenging due to their fast convergence and diversity maintenance requirements. Prediction-based evolutionary algorithms currently gain much attention for meeting these requirements. However, it is not always the case that an elaborate predictor is suitable for different problems and the quality of historical solutions is sufficient to support prediction, which limits the availability of prediction-based methods over various problems. Faced with these issues, this article proposes a knowledge learning strategy for change response in the dynamic multiobjective optimization. Unlike prediction approaches that estimate the future optima from previously obtained solutions, in the proposed strategy, we react to changes via learning from the historical search process. We introduce a method to extract the knowledge within the previous search experience. The extracted knowledge can accelerate convergence as well as introduce diversity for the optimization of the future environment. We conduct a comprehensive experiment on comparing the proposed strategy with the state-of-the-art algorithms. Results demonstrate the better performance of the proposed strategy in terms of solution quality and computational efficiency.
动态多目标优化问题具有快速收敛和多样性维护的要求，因此具有挑战性。基于预测的进化算法目前备受关注，因为它们能够满足这些要求。然而，并非总是如此，一个精心设计的预测器适合不同的问题，并且历史解决方案的质量足以支持预测，这限制了基于预测的方法在各种问题上的可用性。针对这些问题，本文提出了一种用于动态多目标优化中变化响应的知识学习策略。与从先前获得的解决方案中估计未来最优解的预测方法不同，在本文提出的策略中，我们通过从历史搜索过程中学习来应对变化。我们引入了一种从以前的搜索经验中提取知识的方法。提取的知识可以加速收敛，并为未来环境的优化引入多样性。我们通过与最先进的算法进行全面的实验比较，验证了该策略在求解质量和计算效率方面的优越性能。
