Benchmarking and Testing of Hybrid ABC and GA Using Feed Forward Neural Network for TSP
Abstract: The Travelling Salesman Problem is of utmost importance in many areas like Telecommunication, Operation research, Web based Mapping techniques. The Travelling Salesman Problem strives to find the path with shortest distance traversing through each city once and returning back to the original city. Finding an optimal solution can be hard with a normal brute force technique as for an n city case there could be (n-1) ! possible paths all which may not be traversed in a finite time using current computation. The aim of finding an optimal solution is to find the shortest traversing path in minimum number of iterations. This paper puts forward an efficient approach which uses a Feed Forward Neural Network to be used as a Benchmarking and a self -adaptive testing frame for a hybrid of Artificial Bee Colony and Genetic to be used for finding an optimal solution of Travelling Salesman Problem. This algorithm follows a two pronged approach of reducing the iteration for a better result and fine tuning the benchmarking and testing framework.
Keywords: Testing Framework, Benchmarking, optimal, training, error appetite
How to Cite?: Esha Raizada, Dr. S.P Singh, "Benchmarking and Testing of Hybrid ABC and GA Using Feed Forward Neural Network for TSP", Volume 4 Issue 6, June 2015, International Journal of Science and Research (IJSR), Pages: 996-999, https://www.ijsr.net/getabstract.php?paperid=SUB155401, DOI: https://dx.doi.org/10.21275/SUB155401