A Hybrid Particle Swarm Optimization Algorithm for Cell Formation Problem

Cell formation (CF) is a crucial step in design of cellular manufacturing system and has been received considerable research attention over the last three decades. Although different metaheuristic algorithms have been used to solve CF problem, there is only one paper in the literature using particle swarm optimization (PSO) for solving CF problem. This study proposes an effective hybrid PSO combined with genetic operators in order to adapt this method to solve discrete problems such as CF problem. In this approach, PSO is modified by utilizing genetic operators such as crossover and mutation operator to update the particles. The effectiveness of the proposed algorithm is compared with conventional algorithms available in the literature. Computational results using grouping efficacy measure show that the proposed algorithm is highly effective by comparison with six conventional algorithms for CF problem on the same test problems.

Keywords:

Cell Formation , Particle Swarm Optimization , Genetic Algorithm , Grouping Efficacy