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A nature inspired hybrid partitional clustering method based on grey wolf optimization and JAYA algorithm

Shial Gyanaranjan, Saho Sabita, Panigrahi Sibarama

Computer Science

2023

T. 24 (3)

361--405

This paper presents a hybrid meta-heuristic algorithm that uses the grey wolf optimization (GWO) and the JAYA algorithm for data clustering. The idea is to use the explorative capability of the JAYA algorithm in the exploitative phase of GWO to form compact clusters. Here, instead of using only one best and one worst solution for generating offspring, the three best wolves (alpha, beta and delta) and three worst wolves of the population are used. So, the best and worst wolves assist in moving towards the most feasible solutions and simultaneously it helps to avoid from worst solutions; this enhances the chances of trapping at local optimal solutions. The superiority of the proposed algorithm is compared with five promising algorithms; namely, the sine-cosine (SCA),GWO, JAYA, particle swarm optimization (PSO), and k-means algorithms.The performance of the proposed algorithm is evaluated for 23 benchmark mathematical problems using the Friedman and Nemenyi hypothesis tests. Additionally, the superiority and robustness of our proposed algorithm is tested for 15 data clustering problems by using both Duncan's multiple range test and the Nemenyi hypothesis test.

Training RBF NN Using Sine-Cosine Algorithm for Sonar Target Classification

Wang Yixuan, Yuan LiPing, Khishe Mohammad, Moridi Alaveh, Mohammadzade Fallah

Archives of Acoustics

2020

Vol. 45, No. 4

753--764

Radial basis function neural networks (RBF NNs) are one of the most useful tools in the classification of the sonar targets. Despite many abilities of RBF NNs, low accuracy in classification, entrapment in local minima, and slow convergence rate are disadvantages of these networks. In order to overcome these issues, the sine-cosine algorithm (SCA) has been used to train RBF NNs in this work. To evaluate the designed classifier, two benchmark underwater sonar classification problems were used. Also, an experimental underwater target classification was developed to practically evaluate the merits of the RBF-based classifier in dealing with high-dimensional real world problems. In order to have a comprehensive evaluation, the classifier is compared with the gradient descent (GD), gravitational search algorithm (GSA), genetic algorithm (GA), and Kalman filter (KF) algorithms in terms of entrapment in local minima, the accuracy of the classification, and the convergence rate. The results show that the proposed classifier provides a better performance than other compared classifiers as it classifies the sonar datasets 2.72% better than the best benchmark classifier, on average.