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Configuring differential evolution adaptively via path search in a directed acyclic graph for data clustering

机译：通过路径搜索配置差分演进，以用于数据群集的定向非线性图形

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摘要

As an efficient data mining technique, data clustering has been widely-used for data analysis and extracting valuable hidden information. Leveraging the simplicity and effectiveness, the evolutionary optimization-driven clustering algorithms have exhibited promising performance and attracted tremendous attention. Up to the present, how to enable these algorithms to escape from local optima and accelerate convergence rates is an ongoing challenge. In this paper, we propose a novel adaptive Differential Evolution (DE) variant to deal with the above challenge when clustering data. In the improved DE algorithm, the four interdependent components, including mutation strategy, crossover strategy, scaling factor value, and crossover rate, are adaptively configured in an integrated manner via ant colony optimization (ACO) during the problem-solving process. To be specific, the relationships of four components in the DE algorithm are modeled as a directed acyclic graph, and a path in the graph exactly corresponds to a configuration for DE. During the optimization process, ant colony optimization is employed to search for a reasonable path for each individual of DE in terms of pheromones on arcs. In this manner, the configuration of the four interdependent components of DE will be generated dynamically, which is then used to guide the successive search behaviors of individuals in DE. Each individual has a path, representing a configuration for each component. After each iteration, individuals that generate promising solutions are allowed to deposit pheromone on the paths, resulting in more pheromones on the arcs appearing in better algorithm configurations (paths) more frequently. Through this manner, the search strategies and parameters of DE are comprehensively adapted by ACO. The proposed algorithm is named ACODE for short. To verify its effectiveness, the proposed ACODE is compared with four representative data clustering algorithms on eight widely-used benchmark datasets. The experimental results demonstrate the advantages of ACODE over half of the datasets.
机译：作为一种有效的数据挖掘技术，数据群集已被广泛用于数据分析和提取有价值的隐藏信息。利用简单和有效性，进化优化驱动的聚类算法表现出了有希望的性能并引起了巨大的关注。到目前为止，如何启用这些算法从本地Optima逃脱并加速收敛速率是一个持续的挑战。在本文中，我们提出了一种新颖的自适应差分演进（DE）变体来处理聚类数据时的上述挑战。在改进的DE算法中，通过在问题解决过程中通过蚁群优化（ACO）以集成方式自适应地配置四个相互依赖的组件，包括突变策略，交叉策略，缩放因子值和交叉速率。具体而言，DE算法中的四个组件的关系被建模为指向的非循环图，并且图中的路径完全对应于DE的配置。在优化过程中，蚁群优化用于在弧上的信息素方面搜索每个DE的合理路径。以这种方式，将动态地生成DE的四个相互依存组件的配置，然后用于指导DE中的个体的连续搜索行为。每个单独都有一条路径，表示每个组件的配置。在每次迭代之后，允许产生有希望解决方案的个体在路径上存放信息素，导致弧上的更多信息素，以更好的算法配置（路径）更频繁地出现。通过这种方式，DE的搜索策略和参数由ACO全面调整。所提出的算法是命名的ACODE。为了验证其有效性，将建议的acode与八个广泛使用的基准数据集中的四个代表性数据聚类算法进行比较。实验结果表明了Acode超过一半的数据集的优点。

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