Title: Comparison of cuckoo search and particle swarm optimisation in triclustering temporal gene expression data
Authors: P. Swathypriyadharsini; K. Premalatha
Addresses: Department of Information communication and Engineering, Anna University, Chennai, India ' Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode, India
Abstract: The nature inspired meta-heuristic algorithms have ubiquitous nature in nearly every aspect, where computational intelligence is applied. This paper focuses on the comparative study of two commonly used robust bio inspired optimisation algorithms namely cuckoo search and particle swarm optimisation for triclustering the microarray gene expression data. Triclustering broadens the clustering technique by extracting the subset of genes that are highly co-expressed over a subset of conditions and across a subset of time points. Both the algorithms are applied to three real life three dimensional datasets. The performances of the algorithms are compared using the mean square residue as a fitness function and it is also compared with other triclustering algorithms. The experiment results prove that cuckoo search algorithm has better computational efficiency than particle swarm optimisation algorithm.
Keywords: cuckoo search; particle swarm optimisation; PSO; triclustering; microarray gene expression data; temporal data analysis.
International Journal of Swarm Intelligence, 2019 Vol.4 No.1, pp.55 - 72
Available online: 18 Jan 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article