We introduce new diversification methods for zero-one optimization that significantly extend strategies previously introduced in the setting of metaheuristic search. Our methods incorporate easily implemented strategies for partitioning assignments of values to variables, accompanied by processes called augmentation and shifting which create greater flexibility and generality. We then show how the resulting collection of diversified solutions can be further diversified by means of permutation mappings, which equally can be used to generate diversified collections of permutations for applications such as scheduling and routing. These methods can be applied to non-binary vectors using binarization procedures and by diversification-based learning procedures that provide connections to applications in clustering and machine learning. Detailed pseudocode and numerical illustrations are provided to show the operation of our methods and the collections of solutions they create.

中文翻译：

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零一优化的多元化方法

我们为零一优化引入了新的多元化方法，该方法大大扩展了先前在元启发式搜索设置中引入的策略。我们的方法采用了易于实现的策略，用于将值的分配划分为变量，并伴随着称为增强和移位的过程，这些过程可产生更大的灵活性和通用性。然后，我们说明如何通过置换映射进一步使多样化解决方案的结果集合多样化，置换映射同样可以用于为应用程序（例如调度和路由）生成置换的多样化集合。这些方法可以使用二值化过程并通过基于多样性的学习过程应用于非二元向量，这些学习过程提供了到聚类和机器学习中应用程序的连接。