Chaotic quasi-opposition marine predator algorithm for automatic data clustering.

This paper presents a novel approach to data clustering by introducing the Chaotic Quasi-Opposition-Based Learning Marine Predators Algorithm (CQOBLMPA). The primary objective is to enhance the efficiency and accuracy of automatic data clustering by integrating chaotic local search and quasi-opposition-based learning (QOBL) into the Marine Predators Algorithm (MPA). The proposed CQOBLMPA method addresses the slow convergence issue of MPA and improves its ability to avoid local optima. Our contributions include: firstly, an enhanced exploration phase through QOBL to accelerate convergence; secondly, the refinement of the search process using chaotic local search techniques; and thirdly, the development of an automatic clustering framework that does not require prior knowledge of the number of clusters. The effectiveness of CQOBLMPA is demonstrated through extensive tests on 23 global benchmark functions and the CEC2022 benchmarks, showing significant improvements over the original MPA and other metaheuristics. Additionally, the proposed method is validated on eleven real-world and four artificial datasets, achieving superior clustering performance as measured by the Davies–Bouldin index (DB-index) and the Compact-Separated index (CS-index). The results confirm the robustness and efficiency of CQOBLMPA, highlighting its potential for solving complex clustering problems in various domains, with notable performance in the CEC2022 benchmark tests. [ABSTRACT FROM AUTHOR]