Ant Colony Optimization (ACO) algorithm is a novel metaheuristic algorithm that has been widely used for different combinational optimization problem and inspired by the foraging behavior of real ant colonies. Ant Colony Optimization has strong robustness and easy to combine with other methods in optimization. In this paper, an efficient ant colony optimization algorithm with uniform mutation operator using self-adaptive approach has been proposed. Here mutation operator is used for enhancing the algorithm escape from local optima. The algorithm converges to the optimal final solution, by gathering the most effective sub-solutions. Experimental results show that the proposed algorithm is better than the algorithm previously proposed.

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