Li Fang, Dai Congteng, Hussien Abdelazim G, Zheng Rong
School of Humanities, Minnan Science and Technology College, Quanzhou 362332, China.
College of Foreign Languages, Fujian Normal University, Fuzhou 350007, China.
Biomimetics (Basel). 2025 Jun 2;10(6):358. doi: 10.3390/biomimetics10060358.
The Parrot Optimizer (PO) is a new optimization algorithm based on the behaviors of trained Pyrrhura Molinae parrots. In this paper, an improved PO (IPO) is proposed for solving global optimization problems and training the multilayer perceptron. The basic PO is enhanced by using three improvements, which are aerial search strategy, modified staying behavior, and improved communicating behavior. The aerial search strategy is derived from Arctic Puffin Optimization and is employed to enhance the exploration ability of PO. The staying behavior and communicating behavior of PO are modified using random movement and roulette fitness-distance balance selection methods to achieve a better balance between exploration and exploitation. To evaluate the optimization performance of the proposed IPO, twelve CEC2022 test functions and five standard classification datasets are selected for the experimental tests. The results between IPO and the other six well-known optimization algorithms show that IPO has superior performance for solving complex global optimization problems. The results between IPO and the other six well-known optimization algorithms show that IPO has superior performance for solving complex global optimization problems. In addition, IPO has been applied to optimize a multilayer perceptron model for classifying the oral English teaching quality evaluation dataset. An MLP model with a 10-21-3 structure is constructed for the classification of evaluation outcomes. The results show that IPO-MLP outperforms other algorithms with the highest classification accuracy of 88.33%, which proves the effectiveness of the developed method.
鹦鹉优化算法（PO）是一种基于受过训练的派翁尼斯鹦鹉行为的新型优化算法。本文提出了一种改进的PO（IPO）来解决全局优化问题和训练多层感知器。通过使用三种改进方法对基本的PO进行了增强，即空中搜索策略、改进的停留行为和改进的通信行为。空中搜索策略源自北极海鹦优化算法，用于增强PO的探索能力。PO的停留行为和通信行为通过随机移动和轮盘赌适应度-距离平衡选择方法进行修改，以在探索和利用之间实现更好的平衡。为了评估所提出的IPO的优化性能，选择了12个CEC2022测试函数和5个标准分类数据集进行实验测试。IPO与其他六种著名优化算法的结果表明，IPO在解决复杂全局优化问题方面具有卓越性能。此外，IPO已被应用于优化用于对英语口语教学质量评估数据集进行分类的多层感知器模型。构建了一个具有10-21-3结构的MLP模型用于评估结果的分类。结果表明，IPO-MLP以88.33%的最高分类准确率优于其他算法，这证明了所开发方法的有效性。
