Dynamic multiobjective optimization problems (DMOPs) bring more challenges for multiobjective evolutionary algorithm (MOEA) due to its time-varying characteristic. To handle this kind of DMOPs, this paper presents a dynamic MOEA with multiple response strategies based on linear environment detection, called DMOEA-LEM. In this approach, different types of environmental changes are estimated and then the corresponding response strategies are activated to generate an efficient initial population for the new environment. DMOEA-LEM not only detects whether the environmental changes but also estimates the types of linear changes so that different prediction models can be selected to initialize the population when the environmental changes. To study the performance of DMOEA-LEM, a large number of test DMOPs are adopted and the experiments validate the advantages of our algorithm when compared to three state-of-the-art dynamic MOEAs.

中文翻译：

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基于线性环境检测的具有多响应策略的动态多目标优化

动态多目标优化问题（DMOP）由于其时变特性，给多目标进化算法（MOEA）带来了更多挑战。为了处理这种DMOP，本文提出了一种基于线性环境检测的具有多种响应策略的动态MOEA，称为DMOEA-LEM。在这种方法中，估计不同类型的环境变化，然后激活相应的响应策略，以为新环境生成有效的初始种群。DMOEA-LEM不仅可以检测环境变化，还可以估算线性变化的类型，以便在环境变化时可以选择不同的预测模型来初始化种群。为了研究DMOEA-LEM的性能，