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Differential Evolution (DE) is a popular and simple to implement population based stochastic evolutionary algorithm which is used to solve complex optimization problems. In DE, the variation in solutions during the solution search process is controlled by two significant control parameters, namely scale factor (F) and crossover probability (CR). These parameters play important role for balancing the exploration and exploitation capabilities in the solution search region. Therefore, fine tuning of these parameters are very necessary to obtain the global optima. Researchers are continuously working to find a dynamic fine tuning strategy for these parameters. The algorithms having less number of parameters are considered efficient in the field of nature inspired algorithms. Therefore, in this paper, the scale factor (F) parameter of DE is removed from the mutation process equation and by inspired from Gbest-guided ABC, a new mutation equation is proposed. In the proposed mutation equation, the individual will update its position through learning from current global best individual as well as learning from randomly selected individual. The modification is very simple to implement and this simple change in DE's mutation equation shows significant improvement in the algorithm's performance. The proposed algorithm is named as Gbest guided Differential Evolution (Gbest DE) algorithm. Further, the Gbest DE is compared with the basic DE and its recent variant, namely Scale Factor Local Search DE (SFLSDE) over 10 well known test functions.