膀胱癌 (BC) 是一种常见疾病，发病、死亡和费用风险很高。 BC 的主要危险因素包括在工作场所或环境中接触致癌物，尤其是烟草。存在一些困难，例如 BC 分类需要合格的专家。鹦鹉优化器（PO）是一种优化方法，其灵感来自训练有素的鹦鹉中观察到的关键行为，但 PO 算法会陷入子区域，精度较低，错误率较高。因此，结合两种策略开发了 PO (IPO) 算法的改进变体：(1) 镜像反射学习 (MRL) 和 (2) 伯努利图 (BM)。这两种策略都通过避免局部最优并在收敛速度和解决方案多样性之间取得折衷来提高优化性能。根据 2022 年在 IEEE 进化计算大会 (CEC 2022) 测试套件函数和 9 个 BC 上进行的 Friedman 测试和 Bonferroni-Dunn 测试，在统计收敛性和其他指标方面，对拟议 IPO 的性能与其他 8 个竞争对手算法进行了评估来自官方存储库的数据集。 IPO 算法在最佳适应度方面排名第一，并且比 CEC 2022 函数的其他八种 MH 算法更加优化。所提出的 IPO 算法与支持向量机 (SVM) 分类器（称为 IPO-SVM）方法集成，用于膀胱癌分类。然后使用九个 BC 数据集来确认所提出的 IPO 算法的有效性。实验表明 IPO-SVM 方法优于最近提出的八种 MH 算法。使用九个 BC 数据集，IPO-SVM 的准确度 (ACC) 为 84.11%，灵敏度 (SE) 为 98.10%，精密度 (PPV) 为 95.59%，特异性 (SP) 为 95.98%，F 分数 (F1) 94.15%。这表明拟议的 IPO 方法如何有助于有效地对 BC 进行分类。开源代码可在 https://www.mathworks.com/matlabcentral/fileexchange/169846-an-efficient-improved-parrot-optimizer 上获取。版权所有 © 2024 Elsevier Ltd。保留所有权利。

Bladder Cancer (BC) is a common disease that comes with a high risk of morbidity, death, and expense. Primary risk factors for BC include exposure to carcinogens in the workplace or the environment, particularly tobacco. There are several difficulties, such as the requirement for a qualified expert in BC classification. The Parrot Optimizer (PO), is an optimization method inspired by key behaviors observed in trained Pyrrhura Molinae parrots, but the PO algorithm becomes stuck in sub-regions, has less accuracy, and a high error rate. So, an Improved variant of the PO (IPO) algorithm was developed using a combination of two strategies: (1) Mirror Reflection Learning (MRL) and (2) Bernoulli Maps (BMs). Both strategies improve optimization performance by avoiding local optimums and striking a compromise between convergence speed and solution diversity. The performance of the proposed IPO is evaluated against eight other competitor algorithms in terms of statistical convergence and other metrics according to Friedman's test and Bonferroni-Dunn test on the IEEE Congress on Evolutionary Computation conducted in 2022 (CEC 2022) test suite functions and nine BC datasets from official repositories. The IPO algorithm ranked number one in best fitness and is more optimal than the other eight MH algorithms for CEC 2022 functions. The proposed IPO algorithm was integrated with the Support Vector Machine (SVM) classifier termed (IPO-SVM) approach for bladder cancer classification purposes. Nine BC datasets were then used to confirm the effectiveness of the proposed IPO algorithm. The experiments show that the IPO-SVM approach outperforms eight recently proposed MH algorithms. Using the nine BC datasets, IPO-SVM achieved an Accuracy (ACC) of 84.11%, Sensitivity (SE) of 98.10%, Precision (PPV) of 95.59%, Specificity (SP) of 95.98%, and F-score (F1) of 94.15%. This demonstrates how the proposed IPO approach can help to classify BCs effectively. The open-source codes are available at https://www.mathworks.com/matlabcentral/fileexchange/169846-an-efficient-improved-parrot-optimizer.Copyright © 2024 Elsevier Ltd. All rights reserved.