Implementation and Optimization of the N-Queens Problem Using Tabu Search Algorithm

This preprint presents a detailed study on the implementation and optimization of the Tabu Search algorithm for solving the N-Queens problem. The work compares two distinct approaches: the first using control lists for managing swaps and blocking iterations, and the second focusing on swapping only conflicting queens to improve computational efficiency. Experimental results show a significant reduction in convergence time and number of iterations, demonstrating the effectiveness of the optimized approach. The paper also discusses the key elements of the algorithm, including evaluation functions, neighbor generation, and the impact of aspiration criteria.