用于大数据优化的具有二元分层结构和树状拓扑的多目标非洲秃鹫优化算法。

Bo Liu, Yongquan Zhou, Yuanfei Wei, Qifang Luo
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引用次数: 0

摘要

导言:大数据优化(Big-Opt)问题在有效管理和优化大规模数据集固有的分析特性方面提出了独特的挑战。这些问题的复杂性和规模使得传统的数据处理方法显得力不从心:在本研究中,我们提出了一种新的多目标优化算法,即具有二元分层结构和树状拓扑结构的多目标非洲秃鹫优化算法(MO_Tree_BHSAVOA)来解决大数据优化问题:在 MO_Tree_BHSAVOA 算法中,采用了二进制层次结构(BHS),以有效平衡算法中的探索和开发能力;引入了移位密度估计,作为为种群进化提供选择压力的机制;采用了树状拓扑结构,以加强算法摆脱局部最优和保留最优非支配解的能力。利用 CEC 2020 多模式多目标基准函数和 CEC 2021 真实世界约束多目标优化问题评估了所提算法的性能,并将其应用于 Big-Opt 问题:通过与其他多目标优化算法的结果比较,并使用弗里德曼统计检验对性能进行了分析。结果表明,所提出的 MO_Tree_BHSAVOA 算法不仅能提供极具竞争力的结果,而且还优于其他算法:这些发现验证了 MO_Tree_BHSAVOA 在应对与大数据相关的优化挑战方面的有效性和潜在适用性。
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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.

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