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Differential Evolution (DE) is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from slow convergence rate and takes large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution, called Ant Colony Differential Evolution, ACDE. The ACDE algorithm initializes the population using opposition based learning, in mutation phase it applies random localization technique and it simulates the movement of ants to refine the best solution found in each generation. Also, it maintains a single set of population while updating the population for next generation. ACDE validated on a test bed of 7 benchmark problems and two real life problems and the numerical results are compared with original DE. It is found that ACDE requires less computational effort to locate global optimal solution without compromising with the quality of solution.