A hybrid chimp optimization algorithm and generalized normal distribution algorithm with opposition-based learning strategy for solving data clustering problems

This paper focuses on connectivity-based data clustering for categorizing similar and dissimilar data into distinct 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. 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. First, two versions of ChOA with two different dynamic coefficients and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Second, 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. In this study, eight benchmark datasets, including five datasets of the UCI machine learning repository and three challenging shape datasets, are used to investigate the general performance of the proposed method. The results are evaluated against several popular and recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the Sum of Intra-Cluster Distances (SICD), Error Rate (ER), and convergence rate. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.