Li Zhiyong, Chen Hengyong, Xie Zhaoxin, Chen Chao, Sallam Ahmed
College of Information Science and Engineering, Hunan University, Changsha 410082, China.
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.
ScientificWorldJournal. 2014 Jan 30;2014:389742. doi: 10.1155/2014/389742. eCollection 2014.
Many real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Optimization in a changing environment is a challenging task, especially when multiple objectives are required to be optimized simultaneously. Nowadays the common way to solve dynamic multiobjective optimization problems (DMOPs) is to utilize history information to guide future search, but there is no common successful method to solve different DMOPs. In this paper, we define a kind of dynamic multiobjectives problem with translational Paretooptimal set (DMOP-TPS) and propose a new prediction model named ADLM for solving DMOP-TPS. We have tested and compared the proposed prediction model (ADLM) with three traditional prediction models on several classic DMOP-TPS test problems. The simulation results show that our proposed prediction model outperforms other prediction models for DMOP-TPS.
许多实际的优化问题涉及随时间不断变化的目标、约束和参数。在不断变化的环境中进行优化是一项具有挑战性的任务，尤其是当需要同时优化多个目标时。如今，解决动态多目标优化问题（DMOPs）的常用方法是利用历史信息来指导未来的搜索，但目前还没有一种通用的成功方法来解决不同的DMOPs。在本文中，我们定义了一种具有平移帕累托最优集的动态多目标问题（DMOP-TPS），并提出了一种名为ADLM的新预测模型来解决DMOP-TPS。我们在几个经典的DMOP-TPS测试问题上，将所提出的预测模型（ADLM）与三种传统预测模型进行了测试和比较。仿真结果表明，我们提出的预测模型在解决DMOP-TPS方面优于其他预测模型。
