Section Article

Optimising Document Clustering using a Hybrid Genetic-Particle Swarm Model
Author(s): Dr. Yogesh Sundli

Abstract
This paper proposes the use of a revolutionary Evolutionary swarm optimiser to address the problem of document grouping. This algorithm utilises a blend of social and cultural principles derived from the examination of particle swarm optimisation and the concepts of evolution and natural selection (GA). It is capable of solving combinatorial optimisation issues and functions as a population-based heuristic search approach. Genetic algorithms use techniques such as selection reproduction and mutation to generate optimal solutions for subsequent generations. In situations when chromosomes or individuals exhibit a high degree of similarity a local solution may be achieved. The absence of oscillation in traditional PSO might abruptly terminate a particles movement and lead to convergence on suboptimal solutions that may not necessarily be the local optima. This work introduces a new hybrid model for the problem of document clustering. The model combines Particle Swarm Optimisation with Genetic Algorithm to enhance the variety of search results and increase the convergence towards the best possible solution. The model operates concurrently and improves the search process. The efficacy of the approach is validated and assessed using a compilation of document corpora. Based on the findings the technique has significant efficacy and has the potential to serve as a viable alternative to existing methodologies for addressing document clustering challenges.