This study tries to enhance imperialist colony algorithm (ICA) in the context of travel salesman problem (TSP). By adding new learning operator, imperialist learns from colonies that have suitable cost in which manner that improves the solution of problems. We believe that controlled learning improvement is better than uncontrolled one. The efficiency of new operator represented with the variety of instances from TSPLIB. We evaluate the approach on standard TSP test problems and show that it performs better, with respect to solution quality and computation time than ICA without new learning operator
