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A multi-objective African vultures optimization algorithm with binary hierarchical structure and tree topology for
Bo Liu1, Yongquan Zhou2, Yuanfei Wei3
1College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China.
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.
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