Discrete PSO with GA Operators for Document Clustering

The paper presents Discrete PSO algorithm for document clustering problems. This algorithm is hybrid of PSO with GA operators. The proposed system is based on population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of cultural and social rules derived from the analysis of the swarm intelligence (PSO) with GA operators such as crossover and mutation. In standard PSO the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on suboptimal solutions that are not even guaranteed to local optimal solution. In this paper a modification strategy is proposed for the particle swarm optimization (PSO) algorithm and applied in the document corpus. The strategy adds reproduction by using crossover and mutation operators when the stagnation in movement of the particle is identified. Reproduction has the capability to achieve faster convergence and better solution. Experiments results are examined with document corpus. It demonstrates that the proposed DPSO algorithm statistically outperforms the Simple PSO.