An Improved DPSO Algorithm for Cell Formation Problem

Cellular manufacturing systems have been considered as an effective method to increase productivity in industries. For designing of cellular manufacturing systems, several mathematical models and various algorithms have been proposed in the literature. In the present article, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing effectively this problem. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optimum becomes more 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, some numerical examples are introduced. The performance evaluation shows the effectiveness of the DPSO-SA.