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Recent studies have shown that Evolutionary Algorithms have had reasonable success at providing solutions to those problems that fall in NP-Complete class of algorithms. Ant Colony Optimization (ACO) algorithm is one of the promising field of evolutionary algorithms that gave acceptable solutions to Travelling Salesperson Problem and various Network Routing Optimization problems in polynomial time. These classic computer science problems belong to a NP-Complete class of problems that is amongst some of the most interesting in mathematics, including the Sudoku Puzzle Problem. People have tried to automate solving Sudoku Puzzle Problem using brute force, tabu search. Given the success of ACO algorithm with problems within NP-Complete class of problems, it would be interesting to see how it handles this puzzle. A novel technique is presented as modification to standard ACO algorithm. Moreover, we will compare performance matrix (quality of solution and time complexity) of ACO algorithm with other techniques presented in the past to solve the Sudoku puzzle.