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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| Vydané v: | Journal of advanced research Ročník 74; s. 359 - 389 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Egypt
Elsevier B.V
01.08.2025
Elsevier |
| Predmet: | |
| ISSN: | 2090-1232, 2090-1224, 2090-1224 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | [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. |
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| Bibliografia: | 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 |