This study presents a robust optimization algorithm based on updating Ant Colony Optimization (ACO) through hybridization with Artificial Bee Colony (ABC) method and information exchange concept for the purpose of covering ACO limitations in case of an obstacle was on the ants path to food source. The global optimal solution found by the proposed hybrid ACO and ABC (ACOBC) algorithm is considered to be as novel technique to find the shortest path when the vision to food source location is not clear because of an obstacle. Both of the ACO and ABC methods share the globally best solutions through the information exchange process between the ants and bees. Based on the results, the exchange process significantly increases exploration and exploitation of the hybrid method. Besides, a focused elitism scheme is introduced to enhance the performance of the developed algorithm. The validity of the ACOBC method is verified using several continuous test problems and a typical discrete problem, called Traveling Salesman Problem (TSP). The proposed method is found to be a competitive optimization tool for solving continuous and discrete problems. Obstacle model study is very important due to its significance in solving many complex networking problems connected to real human life situations where real data are not available.