一种结合反向学习策略的混合黑猩猩优化算法与广义正态分布算法用于求解数据聚类问题
A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems
AI总结：
本文提出一种融合黑猩猩优化算法、广义正态分布算法与反向学习策略的混合方法ChOAGNDA，用于解决数据聚类中易陷局部最优和收敛慢的问题，实验证明其在聚类精度和收敛速度上优于多种现有方法。
摘要：
本文关注数据聚类问题，旨在基于连通性原则将相似和不相似的数据划分到不同组中，以实现簇的分离。尽管K-means等经典聚类算法是高效的技术，但它们在求解高维问题时往往陷入局部最优，并且收敛速度较慢。为了解决这些问题，许多成功的元启发式优化算法和基于智能的方法被引入，以在合理时间内获得最优解。这些方法通过允许灵活的移动或随机行为来摆脱局部最优问题。在本研究中，我们尝试利用三个主要组成部分来构建一种强大的方法：黑猩猩优化算法（ChOA）、广义正态分布算法（GNDA）和反向学习（OBL）方法。首先，提出了两种具有两种不同独立群策略和七种混沌映射的ChOA版本，分别称为ChOA(I)和ChOA(II)，以实现数据聚类目的下的最佳可能结果。其次，设计了一种结合ChOA和GNDA算法与OBL策略的新颖组合，以解决原始算法的主要不足。最后，所提出的ChOAGNDA方法是一种基于ChOA和GNDA的选择性反向（SO）算法，可用于处理大规模和复杂的现实世界优化问题，特别是数据聚类应用。结果在七个流行的元启发式优化算法和八种最新的最先进聚类技术上进行了评估。实验结果表明，所提出的工作在最小化簇内距离和（SICD）、获得最低错误率（ER）、加快收敛速度以及找到最优聚类中心方面显著优于其他现有方法。
英文摘要：
This paper is concerned with data clustering to separate clusters based on the connectivity principle for categorizing similar and dissimilar data into different groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. They are designed to escape from a local optimum problem by allowing flexible movements or random behaviors. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm (GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of ChOA with two different independent groups' strategies and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Secondly, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. The results are evaluated against seven popular meta-heuristic optimization algorithms and eight recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the achievement in minimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowest Error Rate (ER), accelerating the convergence speed, and finding the optimal cluster centers.
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