Solving N-Queen Problem Using Global Parallel Genetic Algorithm

Great efforts were made to solve uncertain hybrid optimization problems in the past few decades. The n-Queen problem is one of these problems that many solutions have been proposed for. The traditional methods to solve this problem are exponential in terms of runtime and are not acceptable in terms of space and memory complexity. In this study, parallel genetic algorithms are proposed to solve n-Queen problem. Parallelizing island genetic algorithm and the Cellular genetic algorithm was implemented and run. The results show that this algorithm has the ability to find related solutions to this problem. The algorithms are not only faster but also they lead to better performance even without the use of parallel hardware and just running on one core processor. Good comparisons were made between the proposed method and serial genetic algorithms in order to measure the performance of the proposed method. The experimental results show that the algorithm has high efficiency for large-size problems in comparison with genetic algorithms, and, in some cases, it can achieve superlinear speedup. The proposed method, in the present study, can be easily developed to solve other optimization problems.