Abstract

In order to solve the document clustering issue this research introduces a novel Evolutionary swarm optimizer. Using a combination of social and cultural rules gleaned from the study of particle swarm optimization and the ideas of evolution and natural selection (GA) this algorithm can solve combinatorial optimization problems and is essentially a population-based heuristic search method. Genetic algorithms employ operations like selection reproduction and mutation methods to develop optimum solutions for the following generation. Because chromosomes or people with very close resemblance might converge a local solution can be obtained in this situation. The non-oscillatory path in conventional PSO has the potential to rapidly halt a particles motion and converge on less-than-ideal solutions that arent even sure to be the local optimum ones. This study presents a novel hybrid model for the document clustering issue that combines Particle Swarm Optimization with Genetic Algorithm to impr