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Elitist random swapped particle swarm optimization embedded with variable k-nearest neighbour classification: a new PSO variant applied to gene identification

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Abstract

Particle Swarm Optimization (PSO) has a limitation of early convergence and needs to be improved to find the global optima. The main objective here is to improve its exploration capability without deteriorating the exploitation capability. For this purpose, a modified version of PSO, namely Elitist Random Swapped Particle Swarm Optimization (ERSPSO), has been proposed. The elitist (fittest) particles in the swarm guide the other particles to improve their position. To enhance exploration in the search process a swapping of the randomly selected parts of the elitist particle positions (candidate solution) has been made. Consequently, a perturbation is applied to further improve the exploration. The proposed ERSPSO has been applied to the full benchmark set of 25 functions (CEC 2005) as well as complex real life problems like ‘Gene selection by sample classification’. The new variant ERSPSO has been validated by the statistical metrics, convergence plot, sensitivity analysis using convergence behaviour, p-values using Wilcoxon rank sum test and Friedman rank test. For sample classification in Gene selection, VkNN (a new variant of kNN) is proposed which performs better than kNN in classification accuracy. The combined ERSPSO-VkNN is tested in 6 microarray datasets including 4 diseases. In most of the datasets (5 datasets out of 6) ERSPSO-VkNN performs better than the state-of-the-art methods. In different datasets, the percentage of classification accuracy of ERSPSO-VkNN varies between 89.29 and 100%. Finally, a biological verification is performed to show that many of the selected genes are biologically significant according to the reporting in current literature.

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Data availability

The datasets generated and analysed during the current study are not publicly available due the fact that they constitute an excerpt of research in progress but are available from the corresponding author on reasonable request.

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We are grateful to TEQIP-III Maulana AbulKalam Azad University of Technology (MAKAUT), West Bengal, India for supporting our research.

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    Biswajit Jana & Sriyankar Acharyya

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Jana, B., Acharyya, S. Elitist random swapped particle swarm optimization embedded with variable k-nearest neighbour classification: a new PSO variant applied to gene identification. Soft Comput 27, 3169–3201 (2023). https://doi.org/10.1007/s00500-022-07515-9

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