An Improved DPSO Algorithm for Cell Formation Problem

Cellular manufacturing system, an application of group technology, has been considered as an effective method to obtain productivity in a factory. For design of manufacturing cells, several mathematical models and various algorithms have been proposed in literature. In the present research, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing cell formation problem effectively. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optimum becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called discrete particle swarm optimization-simulated annealing (DPSO-SA), based on the idea that PSO ensures fast convergence, while SA brings search out of local optimum. To illustrate the behavior of the proposed model and verify the performance of the algorithm, we introduce a number of numerical examples. The performance evaluation shows the effectiveness of the DPSO-SA.