@inproceedings{scholars2412,
         journal = {Procedia Engineering},
           pages = {629--639},
            year = {2012},
         address = {Jakarta},
           title = {Generating chromatic number of a graph using ant colony optimization and comparing the performance with simulated annealing},
          volume = {50},
            note = {cited By 0; Conference of 2012 International Conference on Advances Science and Contemporary Engineering, ICASCE 2012 ; Conference Date: 24 October 2012 Through 25 October 2012},
             doi = {10.1016/j.proeng.2012.10.069},
          author = {Pal, A. J. and Ray, B. and Zakaria, N. and Naono, K. and Sarma, S. S.},
            issn = {18777058},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891811564&doi=10.1016\%2fj.proeng.2012.10.069&partnerID=40&md5=4fa6ef97e2676aa53b3a75347f901fd6},
        abstract = {The problems which are NP-complete in nature are always attracting the computer scientists to develop some heuristic algorithms, generating optimal solution in time-space efficient manner compared to the existing ones. Generating the chromatic number of a graph in reasonable time belongs to the same category, where the algorithm designers are trying to propose some new algorithms for better result. The interesting feature of this problem is that many real world problems like register allocation, matrix partitioning problem, optical network design etc. can be represented in form of a graph. Once we efficiently generate the chromatic number of the graph, the optimal results of the original problem can be achieved automatically. Here, we have applied Ant Colony Optimization (ACO) for optimal vertex coloring of a simple, symmetric and connected graph (GCP). The algorithm has been tested upon a series of benchmarks including large scale test case, specially the graphs derived from the above mentioned problems, and has shown better output than Simulated Annealing(SA) algorithm on the same problem. Our work is still going on for designing better algorithms generating optimal solutions and applies it to solve other real life problems which can be mapped on the same. {\^A}{\copyright} 2012 Elsevier B.V.}
}