This paper presents a hybrid approach for solving nqueen problem by combination of PSO and SA. PSO is a population based heuristic method that sometimes traps in local maximum. To solve this problem we can use SA. Although SA suffer from many iterations and long time convergence for solving some problems, By good adjusting initial parameters such as temperature and the length of temperature stages SA guarantees convergence. In this article we use discrete PSO (due to nature of n-queen problem) to achieve a good local maximum. Then we use SA to escape from local maximum. The experimental results show that our hybrid method in comparison of SA method converges to result faster, especially for high dimensions n-queen problems.<\/p>\r\n","references":"[1] Stuart Russell, Peter Norvig, \"Artificial Inteligence: A Modern\r\nApproach,\" Constraint Satisfaction Problems, 2nd ed., Pearson\r\nEducation, Inc, Upper Saddle River, New Jersey, 2003,1995, page: 137.\r\n[2] Xiaohui Hu, Russell C. Eberhart, Yuhui Shi, \"Swarm Inteligence for\r\nPermutation Optimization: A case Study of n-Queen Problem\".\r\n[3] Marko Bo\u017eikovic, Marin Golub, Leo Budin, \"Solving n-Queen problem\r\nusing global parallel genetic algorithm\".\r\n[4] J. Dr'eo, A. P'etrowski, P.Siarry, E.Taillard, \"Metaheuristics for Hard\r\nOptimization,\" Some Other Metaheuristics, Springer-Verlag Berlin\r\nHeidelberg 2006, pp. 162-166.\r\n[5] Kwang Y.Lee, Mohamed Al-Sharkawi, \"Modern Heuristic Optimization\r\nTechniques: Theory And Application To Power Systems,\"\r\nFundamentals of Particle Swarm Optimization Techniques, Willey-\r\nInterscience, Hoboken, 2008, pp. 72-79.\r\n[6] Maurice Clerc, \"Particle Swarm Optimization,\" First Formulations,\r\nISTE, United States, 2006, page: 39.\r\n[7] M. Young, The Technical Writer's Handbook. Mill Valley, CA:\r\nUniversity Science, 1989.\r\n[8] Kwang Y.Lee, Mohamed Al-Sharkawi, \"Modern Heuristic Optimization\r\nTechniques: Theory And Application To Power Systems,\" Preface,\r\nWilley-Interscience, Hoboken, 2008, page: xxiv.\r\n[9] J. Dr'eo, A. P'etrowski, P.Siarry, E.Taillard, \"Metaheuristics for Hard\r\nOptimization,\" Simulated Annealing, Springer-Verlag Berlin Heidelberg\r\n2006, pp. 25-31.\r\n[10] J. Dr'eo, A. P'etrowski, P.Siarry, E.Taillard, \"Metaheuristics for Hard\r\nOptimization,\" Introduction, Springer-Verlag Berlin Heidelberg 2006,\r\npage: 8.\r\n[11] Kwang Y.Lee, Mohamed Al-Sharkawi, \"Modern Heuristic Optimization\r\nTechniques: Theory And Application To Power Systems,\"\r\nFundamentals of Simulated Annealing, Willey-Interscience, Hoboken,\r\n2008, page(s): 128 and 129.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 43, 2010"}