similar parts are grouped into families and associated machines into groups as cell formation problem(CF), so that one or more part families can be processed within a single machine group. CM has been proven as a methodology to lower work in process levels, reducing production lead-time while retaining flexibility for new products. The potential benefits include reductions in material handling, setup times, lot sizes, work-inprocess inventories and lead times and increase in throughput, productivity and quality 
(Wemmerlov and Hyer, 1989). 

In CF, a binary machine/part matrix of m p dimension is usually provided. The m rows indicate m machines and the p columns represent p parts. Each binary element in the m p matrix indicates a relationship between parts and machines where ''1'' or ''0'' 
represents that the pth part should be worked on the mth machine or otherwise. The matrix also displays all similarities in parts and machines. The objective is to group parts and machines in a cell based on their similarities. If there are no ''1'' outside the diagonal block and no ''0'' inside the diagonal block then it is called as perfect result. 

That is, the two cells are completely independent where each part family will be processed only within a machine group. On the other hand, if in the machine/part matrix there is ''1'' outside the diagonal block then this is called an ''exceptional part'' because it can work on two or more machine groups, and corresponding machine is called a ''bottleneck machine'' as it processes two or more part families. There may also be a ''0'' inside the diagonal block which is called a ''void''. In general, an optimal result for a machine/part matrix by a CF clustering method is desired to satisfy the following two conditions: (a) To minimize the number of 0s inside the diagonal blocks (i.e., voids); (b) To minimize the number of 1s outside the diagonal blocks (i.e., exceptional elements). There are many cell formation approaches in the literature viz., visual inspection, classification and coding (Singh and Rajamani, 1996), Similarity coefficients(Yin and Yasuda, 2006), Cluster Analysis (Chu and Tsai,1990), array manipulation (Murugan and Selladurai, 2007), Graph Theoretic Approach (Mukhopadhyay, 2009),mathematical programming (Kioon *et al*., 2007), Heuristic algorithms (Mukattash *et al*., 2002), Soft computing technique (Jang *et al*., 2002), Fuzzy clustering (Li, 2007;Tavakkoli- Moghaddam,2007), Metaheuristic techniques like simulated annealing (Lin,2008), genetic algorithm (Pillai *et al*., 2008; Tay and Ho, 2008), Tabu Search (Wang *et al*., 2006), combinatorial search methods(Jeffrey Schaller, 2005), Ants Colony Systems (Kao 2