A multi-objective African vultures optimization algorithm with binary hierarchical structure and tree topology for big data optimization.

INTRODUCTION: Big data optimization (Big-Opt) problems present unique challenges in effectively managing and optimizing the analytical properties inherent in large-scale datasets. The complexity and size of these problems render traditional data processing methods insufficient. OBJECTIVES: In this study, we propose a new multi-objective optimization algorithm called the multi-objective African vulture optimization algorithm with binary hierarchical structure and tree topology (MO_Tree_BHSAVOA) to solve Big-Opt problem. METHODS: In MO_Tree_BHSAVOA, a binary hierarchical structure (BHS) is incorporated to effectively balance exploration and exploitation capabilities within the algorithm; shift density estimation is introduced as a mechanism for providing selection pressure for population evolution; and a tree topology is employed to reinforce the algorithm's ability to escape local optima and preserve optimal non-dominated solutions. The performance of the proposed algorithm is evaluated using CEC 2020 multi-modal multi-objective benchmark functions and CEC 2021 real-world constrained multi-objective optimization problems and is applied to Big-Opt problems. RESULTS: The performance is analyzed by comparing the results obtained with other multi-objective optimization algorithms and using Friedman's statistical test. The results show that the proposed MO_Tree_BHSAVOA not only provides very competitive results, but also outperforms other algorithms. CONCLUSION: These findings validate the effectiveness and potential applicability of MO_Tree_BHSAVOA in addressing the optimization challenges associated with big data.