Rong Miao, Gong Dunwei, Zhang Yong, Jin Yaochu, Pedrycz Witold
IEEE Trans Cybern. 2019 Sep;49(9):3362-3374. doi: 10.1109/TCYB.2018.2842158. Epub 2018 Jun 19.
Various real-world multiobjective optimization problems are dynamic, requiring evolutionary algorithms (EAs) to be able to rapidly track the moving Pareto front of an optimization problem once an environmental change occurs. To this end, several methods have been developed to predict the new location of the moving Pareto set (PS) so that the population can be reinitialized around the predicted location. In this paper, we present a multidirectional prediction strategy to enhance the performance of EAs in solving a dynamic multiobjective optimization problem (DMOP). To more accurately predict the moving location of the PS, the population is clustered into a number of representative groups by a proposed classification strategy, where the number of clusters is adapted according to the intensity of the environmental change. To examine the performance of the developed algorithm, the proposed prediction strategy is compared with four state-of-the-art prediction methods under the framework of particle swarm optimization as well as five popular EAs for dynamic multiobjective optimization. Our experimental results demonstrate that the proposed algorithm can effectively tackle DMOPs.
各种实际的多目标优化问题都是动态的，这就要求进化算法（EA）能够在环境发生变化时迅速跟踪优化问题中移动的帕累托前沿。为此，已经开发了几种方法来预测移动的帕累托集（PS）的新位置，以便种群能够在预测位置周围重新初始化。在本文中，我们提出了一种多方向预测策略，以提高进化算法在解决动态多目标优化问题（DMOP）时的性能。为了更准确地预测帕累托集的移动位置，通过一种提出的分类策略将种群聚类为若干个代表性组，其中聚类的数量根据环境变化的强度进行调整。为了检验所开发算法的性能，在粒子群优化框架下，将所提出的预测策略与四种最先进的预测方法以及五种用于动态多目标优化的流行进化算法进行了比较。我们的实验结果表明，所提出的算法能够有效地处理动态多目标优化问题。
