This research proposes a new wrapper model based on chaos theory and nature-inspired pelican optimization algorithm (POA) for feature selection. The base algorithm is converted into a binary one and a chaotic search to augment POA’s exploration and exploitation process, denoted as chaotic binary pelican optimization algorithm (CBPOA). The main focus of chaos theory is to resolve the slow convergence rate as well as entrapment in local optimal issues of classical POA. Therefore, ten dissimilar chaotic maps are entrenched in POA to tackle these issues and attain a more robust and effective search mechanism. CBPOA executes on continuous search; thus, the continuous search is reformed to a discrete one by adapting transfer functions. In CBPOA, eight transfer functions are used to find the best one and inspect CBPOA. Consequently, the performance of the CBPOA has been investigated by targeting several metrics under 18 UCI datasets. The best variant is nominated and explored the performance with classical wrapper-based and filter-based schemes. Furthermore, the proposed CBPOA is evaluated using 23 functions from CEC-2017, 2018 and 2020 benchmarks. As an outcome, CBPOA has accomplished better outcomes than existing schemes and is superior in handling feature selection problems.

本研究提出了一种基于混沌理论和自然启发鹈鹕优化算法（POA）的新包装模型，用于特征选择。基本算法被转换为二进制算法和混沌搜索，以增强 POA 的探索和开发过程，称为混沌二进制鹈鹕优化算法（CBPOA）。混沌理论的主要焦点是解决经典POA收敛速度慢以及陷入局部最优问题。因此，POA 中包含了 10 个不同的混沌映射来解决这些问题并获得更强大、更有效的搜索机制。CBPOA 执行持续搜索；因此，通过调整传递函数将连续搜索转变为离散搜索。在 CBPOA 中，使用八个传递函数来找到最佳传递函数并检查 CBPOA。最后，CBPOA 的性能已通过针对 18 个 UCI 数据集下的多个指标进行了调查。最佳变体被提名，并通过经典的基于包装器和基于过滤器的方案探索性能。此外，拟议的 CBPOA 使用 CEC-2017、2018 和 2020 基准中的 23 个函数进行评估。结果，CBPOA 取得了比现有方案更好的结果，并且在处理特征选择问题方面更胜一筹。