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

[Display omitted] •The displacement-based density estimation metrics and provide selection pressure is introduced.•A tree topology and enhance the ability of the algorithm to jump out of the local optimum is proposed.•A MO_Tree_BHSAVOA to balance the exploration and exploitation abilities of the alg...

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Bibliographic Details
Published in:	Journal of advanced research Vol. 74; pp. 359 - 389
Main Authors:	Liu, Bo, Zhou, Yongquan, Wei, Yuanfei, Luo, Qifang
Format:	Journal Article
Language:	English
Published:	Egypt Elsevier B.V 01.08.2025 Elsevier
Subjects:	African vulture optimization algorithm (AVOA) Big data optimization Binary hierarchical structure Metaheuristic Multi-objective African vultures optimization algorithm (MO_Tree_BHSAVOA) Original Manuscript Tree topology Multi-objective African vultures optimization algorithm (MO_Tree_BHSAVOA) African vulture optimization algorithm (AVOA) Metaheuristic Big data optimization Binary hierarchical structure Tree topology
ISSN:	2090-1232, 2090-1224, 2090-1224
Online Access:	Get full text
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Summary:	[Display omitted] •The displacement-based density estimation metrics and provide selection pressure is introduced.•A tree topology and enhance the ability of the algorithm to jump out of the local optimum is proposed.•A MO_Tree_BHSAVOA to balance the exploration and exploitation abilities of the algorithm is proposed.•The performance of MO_Tree_BHSAVOA is evaluated using the CEC2020 test suite and compared with several state-of-the-art multi-objective algorithms.•The experiments shows that MO_Tree_BHSAVOA is applied to the Big-Opt problem, and verified to be effective. 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. 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. 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. 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. These findings validate the effectiveness and potential applicability of MO_Tree_BHSAVOA in addressing the optimization challenges associated with big data.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2090-1232 2090-1224 2090-1224
DOI:	10.1016/j.jare.2024.09.019