In this article, a novel fuzzy-based dynamic multiobjective evolutionary algorithm is presented. In this article, giving a valid and true response to the change is an essential task to improve the diversity of solutions when an environmental change occurs. The basic idea is to randomly remove some solutions and replace by newly created solutions. However, the random selection detours the algorithm's trajectory and deteriorates the performance of the optimization algorithm. Recently, the Borda method has been deployed to find the best candidates to be removed from the solutions list. Although the Borda method outperforms the random strategy, it suffers from some drawbacks. In this article, we propose an improved Borda count method incorporated with fuzzy tuned parameters so that its parameters are adjusted by Mamdani fuzzy rules. Our new Borda method can distinguish the information before and after change with different fuzzy weights. In addition to the fuzzy-based Borda, we employ an improved evolutionary algorithm based on fuzzy logic. We propose a novel nondominated sorting genetic algorithm with its parameters tuned with fuzzy rules so that it is adapted to the new environment. Experiments are conducted on standard benchmarks and the results are compared with recent algorithms. Then, multimedia data analysis, such as segmentation of moving objects, is experimented as a dynamic multiobjective problem and solved by the proposed algorithm.

中文翻译：

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基于模糊Borda排序方法的模糊自适应动态NSGA-II及其在多媒体数据分析中的应用

在本文中，提出了一种新的基于模糊的动态多目标进化算法。在本文中，对变化做出有效和真实的响应是在发生环境变化时提高解决方案多样性的一项基本任务。基本思想是随机移除一些解，并用新创建的解替换。然而，随机选择使算法的轨迹走弯路，降低了优化算法的性能。最近，已经部署了 Borda 方法来查找要从解决方案列表中删除的最佳候选者。尽管 Borda 方法优于随机策略，但它也有一些缺点。在本文中，我们提出了一种改进的 Borda 计数方法，它结合了模糊调整参数，以便通过 Mamdani 模糊规则调整其参数。我们新的Borda方法可以区分具有不同模糊权重的变化前后的信息。除了基于模糊的 Borda 之外，我们还采用了基于模糊逻辑的改进进化算法。我们提出了一种新的非支配排序遗传算法，其参数用模糊规则调整，以便适应新环境。在标准基准上进行实验，并将结果与​​最近的算法进行比较。然后，多媒体数据分析，如移动对象的分割，被作为一个动态多目标问题进行实验，并通过所提出的算法解决。我们提出了一种新的非支配排序遗传算法，其参数用模糊规则调整，以便适应新环境。在标准基准上进行实验，并将结果与​​最近的算法进行比较。然后，多媒体数据分析，如移动对象的分割，被作为一个动态多目标问题进行实验，并通过所提出的算法解决。我们提出了一种新的非支配排序遗传算法，其参数用模糊规则调整，以便适应新环境。在标准基准上进行实验，并将结果与​​最近的算法进行比较。然后，多媒体数据分析，如移动对象的分割，被作为一个动态多目标问题进行实验，并通过所提出的算法解决。