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