Shang Gao, Hualong Yu, Ling Qiu, and Cungen Cao


  1. [1] A.N. Antamoshkin and L.A. Kazakovtsev, Random search algorithm for the p-median problem, Informatica (Slovenia), 37(3), 2013, 267–278.
  2. [2] R. Kumar, P.T. Kabamba, and D.C. Hyland, Analysis and parameter selection for an adaptive random search algorithm, Proc. 43rd IEEE Conf. on Decision and Control, Nassau, Bahamas, Vol. 5, 2004, 5322–5327.
  3. [3] N.K. Krivulin, D. Guster, and C. Hall, On parallel implementation of a discrete optimization random search algorithm, WSEAS Transactions on Computers, 4(9), 2005, 1122–1129.
  4. [4] S. Tu, Z. Wu, and Z.Y. Wu, The adaptive stochastic resonance signal detection system based on the multi-point random search algorithm, Applied Mechanics and Materials, 339, 2013, 409–415.
  5. [5] S. Khatun, K.F. Rabbi, C.Y. Yaakub, and M.F.J. Klaib, A random search based effective algorithm for pairwise test data generation, ECCE 2011 – Intern. Conf. on Electrical, Control and Computer Engineering, Kuantan, Malaysia, 2011, 293–297.
  6. [6] G. Gao, L. Shen, and W. Chang, Using simulated annealing algorithm with search space sharpening to solve traveling salesman problem, Acta Automatica Sinica, 25(3), 1999, 425–428 (in Chinese).
  7. [7] S. Soares, C.H. Antunes, and R. Araújo, Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development, Neurocomputing, 121, 2013, 498–511.
  8. [8] S. Arunachalam, R. Saranya, and N. Sangeetha, Hybrid artificial bee colony algorithm and simulated annealing algorithm for combined economic and emission dispatch including valve point effect, in Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), Springer Verlag, Vol. LNCS 8297, Part 1 (2013), 354–365.
  9. [9] X. Wei, Parameters setting and analysis for ant colony optimization algorithm, Energy Education Science and Technology Part A: Energy Science and Research, 31(1), 2013, 417–420.
  10. [10] W. Liu, An improved ant colony algorithm for continuous domain and its convergence analysis, International Journal of Applied Mathematics and Statistics, 48(18), 2013, 356–363.
  11. [11] A.M. Mora, P. Garc´ıa-Sánchez, J.J. Merelo, and P.A. Castillo, Pareto-based multi-colony multi-objective ant colony optimization algorithms: An island model proposal, Soft Computing, 17(7), 2013, 1175–1207.
  12. [12] R.C. Eberhart and J. Kennedy, A new optimizer using particles swarm theory, Proc. Sixth Intern. Symp. on Micro Machine and Human Science, Nagoya, Japan, 1995, 39–43.
  13. [13] Y.H. Shi and R.C. Eberhart, A modified particle swarm optimizer, IEEE Intern. Conf. on Evolutionary Computation, Anchorage, Alaska, May 4–9, 1998, 69–73.
  14. [14] J. Kennedy and R. Eberhart, Particle swarm optimization, Proc. IEEE Intern. Conf. on Neural Networks, Perth, 1995, 1942–1948.
  15. [15] S. Gao and J. Yang, Swarm intelligence algorithms and applications (Beijing: China Water and Power Press, 2006), 112–117 (in Chinese).
  16. [16] S. Gao, Z.Y. Zhang, and C.G. Cao, A novel ant colony genetic hybrid algorithm, Journal of Software, 5(11), 2010, 1179–1186.
  17. [17] S. Gao, L. Qiu, and C.G. Cao, Estimation of distribution algorithms for knapsack problem, Journal of Software, 9(1), 2014, 104–110.
  18. [18] S. Gao and J. Yang, Research on chaos particle swarm optimization algorithm, Pattern Recognition and Artificial Intelligence, 19(1), 2006, 266–270 (in Chinese).
  19. [19] S. Gao, The optimization calculation based on Matlab GA toolbox, Microcomputer Applications, 18(8), 2002, 52–54 (in Chinese).

Important Links:

Go Back