Department of Computer Science and Engineering, Punjabi University, Patiala, Patiala, 147002, India.
Sci Rep. 2021 Nov 9;11(1):21933. doi: 10.1038/s41598-021-01018-7.
Swarm intelligence techniques have a vast range of real world applications.Some applications are in the domain of medical data mining where, main attention is on structure models for the classification and expectation of numerous diseases. These biomedical applications have grabbed the interest of numerous researchers because these are most serious and prevalent causes of death among the human whole world out of which breast cancer is the most serious issue. Mammography is the initial screening assessment of breast cancer. In this study, an enhanced version of Harris Hawks Optimization (HHO) approach has been developed for biomedical databases, known as DLHO. This approach has been introduced by integrating the merits of dimension learning-based hunting (DLH) search strategy with HHO. The main objective of this study is to alleviate the lack of crowd diversity, premature convergence of the HHO and the imbalance amid the exploration and exploitation. DLH search strategy utilizes a dissimilar method to paradigm a neighborhood for each search member in which the neighboring information can be shared amid search agents. This strategy helps in maintaining the diversity and the balance amid global and local search. To evaluate the DLHO lot of experiments have been taken such as (i) the performance of optimizers have analysed by using 29-CEC -2017 test suites, (ii) to demonstrate the effectiveness of the DLHO it has been tested on different biomedical databases out of which we have used two different databases for Breast i.e. MIAS and second database has been taken from the University of California at Irvine (UCI) Machine Learning Repository.Also to test the robustness of the proposed method its been tested on two other databases of such as Balloon and Heart taken from the UCI Machine Learning Repository. All the results are in the favour of the proposed technique.
群体智能技术在现实世界中有广泛的应用。一些应用是在医学数据挖掘领域，主要关注的是用于分类和预测多种疾病的结构模型。这些生物医学应用引起了众多研究人员的兴趣，因为这些疾病是全世界人类死亡的主要和常见原因，其中乳腺癌是最严重的问题。乳房 X 线照相术是乳腺癌的初步筛查评估。在这项研究中，为生物医学数据库开发了一种增强型哈里斯鹰优化（HHO）方法，称为 DLHO。该方法通过将基于维度学习的搜索（DLH）搜索策略的优点与 HHO 相结合而引入。本研究的主要目的是缓解 HHO 的群体多样性不足、过早收敛以及探索和开发之间的不平衡。DLH 搜索策略利用一种不同的方法来为每个搜索成员构建邻域，其中可以在搜索代理之间共享邻域信息。这种策略有助于维持全局搜索和局部搜索之间的多样性和平衡。为了评估 DLHO，进行了大量实验，例如（i）使用 29-CEC-2017 测试套件分析优化器的性能，（ii）通过在不同的生物医学数据库上测试 DLHO 来证明其有效性，其中我们使用了两个不同的乳房数据库，即 MIAS，第二个数据库来自加利福尼亚大学欧文分校（UCI）机器学习知识库。还为了测试所提出方法的鲁棒性，它已经在来自 UCI 机器学习知识库的另外两个数据库，如气球和心脏上进行了测试。所有结果都支持所提出的技术。
