Particle Swarm Optimization Using Blended Crossover Operator

In recent days, Swarm Intelligence plays an important role in solving many real life optimization problems. Particle Swarm Optimization (PSO) is swarm intelligence based search and optimization algorithm which is used to solve global optimization problems. But due to lack of population diversity and premature convergence it is often trapped into local optima. We can increase diversity and prevent premature convergence by using some evolutionary operators in PSO. In this paper the blend crossover operator is used to increase the search ability of the swarm in the search space .Particle Swarm Optimization uses this crossover technique to converge optimum solution fast .Thus the blend crossover operator is combined with particle swarm optimization to enhance the performance and keep the diversity which guides the particles to the global optimum efficiently. In this paper a blended crossover based particle swarm optimization algorithm (BCPSO) has been proposed and the proposed algorithm has been tested with some standard benchmark functions. Result shows the promising performance of the algorithm.