The N-Queens Problem

The n-queens problem is a generalization of the eight-queens problem of placing eight queens on a standard chessboard so that no queen attacks any other queen. The original eight-queens problem was first posed in 1848 by Bezzel, a German chess player, in the Berliner Schachzeitung (or the Berlin Chess Newspaper). The generalization is due to Linolet, who asked the same question later in 1869, but now for n queens on an n x n board. The problem still retains much fascination, and continues to be studied. Why study this problem if it has already been solved? It was initially studied for “mathematical recreation.” However today, the problem is applied in parallel memory storage schemes, VLSI testing, traffic control and deadlock prevention in concurrent programming. Other applications include neural networks, constraint satisfaction problems, image processing, motion estimation in video coding, and error-correcting codes. Additionally, the problem appears naturally in biology, where it was observed that the computation involved in the analysis of the secondary structure of nucleic acids is analogous to that involved in finding solutions to the n-queens problem

Parallelization of the N-queens problem : Load unbalance analysis.

The paper presents an analysis of three parallelization structures of the N-queens problem, taking into account N processors. The focus has been set on investigating the adaptation of the architecture structure to the proposed algorithm type, so as to study the load unbalance in each case, for which two different metrics have been established. The experimental results and the efficient implementation of the algorithms are discussed together with the related current research lines.Eje: Procesamiento distribuido y paralelo (PDP)Red de Universidades con Carreras en Informática (RedUNCI

The N queens problem - new variants of the Wirth algorithm

The paper presents new ways of n-queens problem solving . Briefly,this is a problem on a nxn chessboard of a set n-queens, so that any two of themare not in check. At the beginning, currently used algorithm to find solutions isdiscussed. Then sequentially 4 new algorithms, along with the interpretation ofchanges are given. The research results, including comparison, of calculation timesof all algorithms together with their interpretation are discussed. Finally, conclusionsare given. The results were obtained thanks to the pre-created application.Chapters except for By filtering ver. 2 were based on the previous studies carriedout during the Bachelor course [1]

Swapping algorithm and meta-heuristic solutions for combinatorial optimization n-queens problem

This research proposes the swapping algorithm a new algorithm for solving the n-queens problem, and provides data from experimental performance results of this new algorithm. A summary is also provided of various meta-heuristic approaches which have been used to solve the n-queens problem including neural networks, evolutionary algorithms, genetic programming, and recently Imperialist Competitive Algorithm (ICA). Currently the Cooperative PSO algorithm is the best algorithm in the literature for finding the first valid solution. Also the research looks into the effect of the number of hidden nodes and layers within neural networks and the effect on the time taken to find a solution. This paper proposes a new swapping algorithm which swaps the position of queens

A Solution to the N-Queens Problem Using Biogeography-Based Optimization

Biogeography-based Optimization (BBO) is a global optimization algorithm based on population, governed by mathematics of biogeography, and dealing with geographical distribution of biological organisms. The BBO algorithm was used in the present study to provide a solution for the N-queens problem. The performance of the proposed algorithm has been evaluated in terms of the quality of the obtained results, cost function, and execution time. Furthermore, the results of this algorithm were compared against those of genetic and particle swarm algorithms