Non-swarm intelligence algorithms: a case study

Abstract

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.