The case study of plant intelligence inspired novel non-swarm intelligence (NSI) algorithms, namely Venus Flytrap Optimization and Bladder-Worts Suction, concentrated in this paper. These algorithms devised on the prey-hunting mechanisms of the Venus Flytrap (Dionaea Muscipula) and BladderWorts (Utricularia) plants, respectively. A comparative view of these algorithms is discussed. The main-support criterion is the major characteristic of these approaches. The benefits of this main-support criterion and their performances are evidenced with a case study of extracting the highly correlated maximal local patterns in gene expression data through biclustering. The NSI algorithms are proposed for biclustering gene expression data in this paper. The results are compared with existing optimization techniques like PSO and GA, and biclustering approaches like Cheng and Church, OPSM, BiMax, and Plaid approaches. This analysis evidenced the performance of NSI algorithms can yield optimal maximal local patterns with high correlation. Further, various real-time research applications of NSI approaches are also discussed.

中文翻译：

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非群体智能算法：案例研究

植物智能的案例​​研究启发了新的非群体智能 (NSI) 算法，即捕蝇草优化和膀胱麦汁抽吸，集中在本文中。这些算法分别针对捕蝇草 (Dionaea Muscipula) 和 BladderWorts (Utricularia) 植物的猎物机制设计。讨论了这些算法的比较视图。主要支持标准是这些方法的主要特征。通过双聚类提取基因表达数据中高度相关的最大局部模式的案例研究证明了这一主要支持标准的好处及其性能。本文提出了用于双聚类基因表达数据的 NSI 算法。将结果与现有的优化技术（如 PSO 和 GA）进行比较，和双聚类方法，如 Cheng 和 Church、OPSM、BiMax 和 Plaid 方法。该分析证明 NSI 算法的性能可以产生具有高相关性的最优最大局部模式。此外，还讨论了 NSI 方法的各种实时研究应用。