The Cell-formation problem (CFP) addresses the issue of creation of part families based on similarity in processing requirements and the grouping of machines into groups, based on their ability to process those specific part families. The CFP is combinatorial in nature and due to difficulty faced in solving related mathematical programming problems, efforts have been made to use evolutionary approaches.  Literature highlights that there are many advantages of converting batch type manufacturing system (BTMS) in cellular manufacturing system (CMS). In this paper, mathematical model has been proposed for groups to be emerged naturally. As this mathematical model of CFP becomes NP- complete in nature, researchers advocated the use of meta-heuristics. Over the years, many different meta-heuristic methods have been used to solve the CFP in group technology application. In the present paper, evolutionary population based method known as Particle swarm optimization (PSO) hybridized with assignment algorithm is used to solve cell formation problems. Due to these proposed changes, efficiencies of cell formed are significantly increased in comparison to the result available in the literature. Proposed hybrid algorithm is applied to solve 30 different types of randomly generated and 10 standard CFP comprising of large verity in terms of number of machine, number of parts and number of machines required by parts. For this algorithm, optimal values of parameter were found with the use of Taguchi method. It is found that the proposed changes in algorithm and parameters obtained significantly impact the results in terms of efficiency values.