Visualization of Frequently Changed Patterns Based on the Behaviour of Dung Beetles

Nature serves as a source of motivation for the development of new approaches to solve real life problems such as minimizing the computation time on visualization of frequently changed patterns from datasets. An approach adopted is the use of evolutionary algorithm based on swarm intelligence. This evolutionary algorithm is a computational approach that is based on the characteristics of dung beetles in moving dung with limited computational power. The contribution of this paper is the mathematical formulation of the unique characteristics of dung beetles (that is, path integration with replusion and attraction of trace, dance during orientation and ball rolling on straight line) in creating imaginary homes after displacement of its food (dung) source. The mathematical formulation is translated into an algorithmic structure that search for the best possible path and display patterns using simple two dimensional view. The computational time and optimal value are the techniques to select the best visualization algorithm (between the proposed dung beetle algorithm and comparative algorithms –that is Bee and ACO). The analysis shows that dung beetle algorithm has mean computational time of 0.510, Bee has 2.189 and ACO for data visualization has 0.978. While, the mean optimal value for bung beetle is 0.000117, Bee algorithm is 2.46E−08 and ACO for data visualization is 6.73E−13. The results indicates that dung beetle algorithm uses minimum computation time for data visualization.

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