[1]
Michael, J., Gerhard,Rand. and Giovanni,R., The Traveling Salesman Problem, Handbooks in Operations Research and Management Science, 7, 1995, 225 - 330.
Google Scholar
[2]
Munakata, T., Y, Nakamura. Temperature control for simulated annealing, Physical Review 64, 2001, 46 - 127.
Google Scholar
[3]
Larranaga, P., Kuijpers, C.M.H., Murga, R.H., Inza, I. and Dizdarevic,S., Genetic Algorithms for the Traveling Salesman Problem: A Review of Representations and Operators, Artificial Intelligence Review. 13, 1999, 129 - 170.
DOI: 10.1023/a:1006529012972
Google Scholar
[4]
Dorigo,M., Gambardella, L.M., Ant colony system: a cooperative learning approach to the traveling salesman problem, Evolutionary Computation, IEEE Transactions, 1 ,1997, 53 - 66.
DOI: 10.1109/4235.585892
Google Scholar
[5]
W.L. Zhong, J. Zhang and W.N. Chen, A Novel Discrete Particle Swarm Optimization to solve Traveling Salesman Problem, IEEE Congress on Evolutionary Computation, 2007, 3283 - 3287.
DOI: 10.1109/cec.2007.4424894
Google Scholar
[6]
Karaboga, D., An idea based on honey bee swarm for numerical optimization, Technical Report TR06, Computer Engineering Department, Erciyes University,Turkey, 2005.
Google Scholar
[7]
Karaboga, D., Basturk, B., A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization, 39, 2007, 459–471.
DOI: 10.1007/s10898-007-9149-x
Google Scholar
[8]
Karaboga, D., Basturk, B., On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing, 8, 2008, 687 - 697.
DOI: 10.1016/j.asoc.2007.05.007
Google Scholar
[9]
Karaboga, D., Basturk, B., Artificial Bee Colony (ABC) algorithm for Solving Constrained Optimization Problem, Foundations of Fuzzy Logic and Soft Computing, 4529, 2007, 789 – 798.
DOI: 10.1007/978-3-540-72950-1_77
Google Scholar
[10]
Pulikanti, S., Singh, A., An Artificial Bee Colony Algorithm for the Quadratic Knapsack Problem, Neural information processing, 5864, 2009, 196 – 295.
DOI: 10.1007/978-3-642-10684-2_22
Google Scholar
[11]
Singh, A., An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem, Applied Soft Computing, 9, 2009, 625 – 631.
DOI: 10.1016/j.asoc.2008.09.001
Google Scholar
[12]
Baykasoglu, A., Ozbakir, L., Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem, Focus on Ant and Particle Swarm Optimization, 2007, 114 – 144.
DOI: 10.5772/5101
Google Scholar
[13]
Sundar, S., Singh, A., A swarm intelligence approach to the quadratic minimum spanning, Information Science, 17,2010, 3182 – 3191.
DOI: 10.1016/j.ins.2010.05.001
Google Scholar
[14]
Rosenkrantz, D.J., Stearns, R.E. and Lewis, P.M., An analysis of several heuristics for the traveling salesman problem, Fundamental Problem in Computing, 1, 2009, 45 – 69.
DOI: 10.1007/978-1-4020-9688-4_3
Google Scholar
[15]
Tao, G., Michalewicz, Z., Inver-over operator for the TSP, Parallel Problem Solving from Nature, 1498, 1998, 803 – 812.
DOI: 10.1007/bfb0056922
Google Scholar
[16]
Lin, S., Kerninghan, B.W., An Effective Heuristic Algorithm for the Traveling Salesman Problem. Operations Research, 1972, 498 – 516.
DOI: 10.1287/opre.21.2.498
Google Scholar
[17]
G. Reinelt, TSPLIB—A traveling salesman problem library, ORSA Journal on Computing, 3, 1991, 376–384.
DOI: 10.1287/ijoc.3.4.376
Google Scholar
[18]
Wong L. P., Low, M.Y.H. and Chong, C.S., A bee colony optimization algorithm for traveling salesman problem, in Proceedings of Second Asia International Conference on Modeling and Simulation, 2008, 818-823.
DOI: 10.1109/ams.2008.27
Google Scholar
