Three Strategies Enhance the Bionic Coati Optimization Algorithm for Global Optimization and Feature Selection Problems.

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Qingzheng Cao, Shuqi Yuan, Yi Fang
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Abstract

With the advancement of industrial digitization, utilizing large datasets for model training to boost performance is a pivotal technical approach for industry progress. However, raw training datasets often contain abundant redundant features, which increase model training's computational cost and impair generalization ability. To tackle this, this study proposes the bionic ABCCOA algorithm, an enhanced version of the bionic Coati Optimization Algorithm (COA), to improve redundant feature elimination in datasets. To address the bionic COA's inadequate global search performance in feature selection (FS) problems, leading to lower classification accuracy, an adaptive search strategy is introduced. This strategy combines individual learning capability and the learnability of disparities, enhancing global exploration. For the imbalance between the exploration and exploitation phases in the bionic COA algorithm when solving FS problems, which often traps it in suboptimal feature subsets, a balancing factor is proposed. By integrating phase control and dynamic adjustability, a good balance between the two phases is achieved, reducing the likelihood of getting stuck in suboptimal subsets. Additionally, to counter the bionic COA's insufficient local exploitation performance in FS problems, increasing classification error rates, a centroid guidance strategy is presented. By combining population centroid guidance and fractional-order historical memory, the algorithm lowers the classification error rate of feature subsets and speeds up convergence. The bionic ABCCOA algorithm was tested on the CEC2020 test functions and engineering problem, achieving an over 90% optimization success rate and faster convergence, confirming its efficiency. Applied to 27 FS problems, it outperformed comparative algorithms in best, average, and worst fitness function values, classification accuracy, feature subset size, and running time, proving it an efficient and robust FS algorithm.

三种策略增强了全局优化和特征选择问题的仿生Coati优化算法。
随着工业数字化的推进,利用大数据集进行模型训练以提高性能是行业进步的关键技术途径。然而,原始训练数据集往往包含大量冗余特征,增加了模型训练的计算成本,影响了模型的泛化能力。为了解决这一问题,本研究提出了仿生ABCCOA算法,即仿生Coati优化算法(COA)的增强版本,以改进数据集的冗余特征消除。针对仿生COA在特征选择(FS)问题中全局搜索性能不足导致分类精度降低的问题,提出了一种自适应搜索策略。这一策略将个体学习能力与差异的可学性相结合,加强了全球探索。针对仿生COA算法在求解FS问题时,探索阶段和开发阶段之间存在不平衡,容易陷入次优特征子集的问题,提出了平衡因子。通过整合相位控制和动态可调性,实现了两个阶段之间的良好平衡,减少了陷入次优子集的可能性。此外,针对仿生COA在FS问题中局部开发性能不足,分类错误率增加的问题,提出了一种质心制导策略。该算法将种群质心引导和分数阶历史记忆相结合,降低了特征子集的分类错误率,加快了收敛速度。仿生ABCCOA算法在CEC2020测试函数和工程问题上进行了测试,优化成功率超过90%,收敛速度更快,验证了算法的有效性。应用于27个FS问题,在最佳、平均和最差适应度函数值、分类精度、特征子集大小和运行时间等方面优于比较算法,证明了该算法是一种高效、鲁棒的FS算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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