This paper proposes a new prediction-based dynamic multiobjective optimization (PBDMO) method, which combines a new prediction-based reaction mechanism and a popular regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) for solving dynamic multiobjective optimization problems. Whenever a change is detected, PBDMO reacts effectively to it by generating three subpopulations based on different strategies. The first subpopulation is created by moving nondominated individuals using a simple linear prediction model with different step sizes. The second subpopulation consists of some individuals generated by a novel sampling strategy to improve population convergence as well as distribution. The third subpopulation comprises some individuals generated using a shrinking strategy based on the probability distribution of variables. These subpopulations are tailored to form a population for the new environment. The experimental results carried out on a variety of bi- and three-objective benchmark functions demonstrate that the proposed technique has competitive performance compared with some state-of-the-art algorithms.

中文翻译：

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动态多目标优化的新预测策略

本文提出了一种新的基于预测的动态多目标优化 (PBDMO) 方法，该方法结合了新的基于预测的反应机制和流行的基于正则模型的多目标分布估计算法 (RM-MEDA)，用于解决动态多目标优化问题。每当检测到变化时，PBDMO 都会根据不同的策略生成三个亚群，从而对其做出有效的反应。第一个子群是通过使用具有不同步长的简单线性预测模型移动非支配个体来创建的。第二个子种群由一些通过新的抽样策略生成的个体组成，以改善种群收敛和分布。第三个子群包括一些使用基于变量概率分布的收缩策略生成的个体。这些亚群经过调整以形成新环境的群。在各种双目标和三目标基准函数上进行的实验结果表明，与一些最先进的算法相比，所提出的技术具有竞争性能。