![]() For easy-, medium-, and hard-level puzzles, Filtered-VNS shows better solution quality than Unfiltered-VNS. The experimental results indicate that our models can solve all benchmarks. Two proposed models with the best configurations are tested on 57 well-known Sudoku benchmarks. In both models, the neighborhood structures are implemented by using different local search improvement strategies. Local search is performed by a novel mutation-based. The Filtered-VNS, which is the second model, uses filtering to reduce the number of partial infeasible solutions in the search area. For the first model (Unfiltered-VNS), four neighborhood structures are proposed. In this paper, two novel models based on the Variable Neighborhood Search (VNS) algorithm are proposed to solve Sudoku puzzles. 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. A novel technique is presented as modification to standard ACO algorithm. 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. People have tried to automate solving Sudoku Puzzle Problem using brute force, tabu search. 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. 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. 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. Except for two of the 57 benchmarks, Filtered-VNS improves the solution qualities of the previous studies. For very hard instances, performance of Unfiltered-VNS is better than Filtered-VNS. Local search is performed by a novel mutation-based neighborhood structure. A critical appraisal of the observed behavior of GA is presented in this paper, covering combinations of two mutations and three crossovers schemes. On a positive note, GA was able to solve the Sudoku problems much faster, only the Sudoku had very few unfilled elements. The findings reveal that GA is ineffective to deal with the Sudoku problem, as compared to other classical algorithms, as it often fails to disengage itself from some local optimum condition. A comparative study on the performance of GA with these schemes was conducted involving Sudoku. The investigation includes various mutations and crossover schemes to unravel the Sudoku problem. This paper presents a study on the capability of the Genetic Algorithm (GA) to solve the classical Sudoku problem. Genetic Algorithm (GA), one of the instances of EAs, has potential research avenues of testing its applicability in real-world problems and improving its performance. Prospective optimization tools such as Evolutionary Algorithms (EAs), are widely used to tackle optimization problems in the real world. ![]()
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