Randomized bias genetic algorithm to solve traveling salesman problem

Abstract

Genetic algorithm is powerful technique to discover optimal for traveling salesman problem (TSP). Traveling salesman problem is well known NP-hard problem whose applications in many industries like logistic, transportation, manufacturing etc. Attempts has made to modify the genetic algorithm with parent selection in randomized bias (RBGA) manner to discover the optimal for traveling salesman problem. RBGA performance has tested against GA with respect to 30 benchmark problems for traveling salesman, which contain the suite of symmetric and non-symmetric problems. Numerical and graphical results show that RBGA have major advancement over GA for TSP in relation to find optimal and CPU running time. The TSP data set used for simulation is available at http://people.sc.fsu.edu/jburkardt/datasets/tsp/tsp.html.