A Hybrid Between TOA and Lévy Flight Trajectory for Solving Different Cluster Problems

In data analysis applications for extraction of useful knowledge, clustering plays an important role. The major shortcoming of traditional clustering algorithms is exhibiting poor performance in solving complex data cluster problems. This research paper introduces a novel hybrid optimization technique-based clustering approach. This paper is designed with two main objectives: designing efficient function optimization algorithm and developing advanced data clustering approach. In achieving the first objective, the standard TOA is first enhanced by hybridizing with Lévy flight trajectory and benchmarked on 23 functions. A new clustering approach is developed by conjoining k-means algorithm and Lévy flight TOA. The numerical complexity of the proposed novel clustering approach was tested on 10 UCI clustering datasets and four web document cluster problems. Several simulation experiments were conducted and an analysis of the results was done. The obtained graphical and statistical analysis reveals that the proposed novel clustering approach yields better quality clusters. based hybrid TOA for solving global function optimization problems as well as different data cluster problems. From the simulation experiments and analysis the proposed clustering approach is a suitable addition to clustering domains for solving complex data clustering problems. The NFL theorem logically proved that there is not any single optimization technique existed that can solve all sorts of optimization problems. In this work Lévy flight trajectory algorithm was used to enhance the standard TOA. In future work, other performance boosting up methods can be investigated. The future research also can development of new and novel nature-inspired Metaheuristics.