The two most commonly used local search operators in heuristic or metaheuristic approaches to solve combinatorial optimization problems are genetic search operators and neighborhood search operators. In this work both the operators are combined to develop two modified artificial bee colony algorithms (ABC) for solving combinatorial optimization problems. We used those modified ABCs to solve some standard quadratic assignment problems (QAP) available in QAP library. The performance of the algorithms are compared with some state of the art algorithms by means of percentage variation from the known optima and minimum time requirement to reach the optima.