Evaluation and comparison of brute-force search and constrained optimization algorithms to solve the N-Queens problem

DOI10.1007/978-3-031-08266-5_9OpenAlexW4312913266MaRDI QIDQ2101278

Oscar Montiel, Ulises Orozco-Rosas, Alfredo Arteaga, Oscar Castillo

Publication date: 5 December 2022

Full work available at URL: https://doi.org/10.1007/978-3-031-08266-5_9

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59)

Uses Software

• CUDA
• GLPK

Cites Work

• A survey of known results and research areas for \(n\)-queens
• Linear congruence equations for the solutions of the \(N\)-queens problem
• A maximum neural network approach for \(N\)-queens problems
• A theoretical evaluation of selected backtracking algorithms.
• A Branch and Bound Clustering Algorithm
• The n-Queens Problem
• Development of neurofuzzy architecture for solving theN-Queens problem

This page was built for publication: Evaluation and comparison of brute-force search and constrained optimization algorithms to solve the N-Queens problem