Open Access
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 4, Number 1, January 2010
Page(s) 27 - 32
DOI https://doi.org/10.1051/ijsmdo/2010004
Published online 21 July 2011
  1. J. Behnamian, S.M.T. Fatemi Ghomi. Development of a PSO-SA hybrid metaheuristic for a new comprehensive regression model to time-series forecasting, Expert Systems with Applications, 974-984, (2010). [Google Scholar]
  2. V. Savsani, R.V. Rao, D.P. Vakharia. Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms, Mechanism and Machine Theory, 531-541, (2010). [Google Scholar]
  3. M.M. Ali, M.N. Gabere. A simulated annealing driven multi-start algorithm for bound constrained global optimization, Journal of Computational and Applied Mathematics, 2661-2674, (2010). [Google Scholar]
  4. T. Niknam, B. Amiri, J. Olamaei, A. Arefi. An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering, Journal of Zhejiang University SCIENCE, 512-519, (2009). [Google Scholar]
  5. S. Sitarz. Ant algorithms and simulated annealing for multicriteria dynamic programming, Computers & Operations Research, 433-441, (2009). [Google Scholar]
  6. Y. Zhaoa, W. Zub, H. Zeng. A modified oarticle swarm optimization via particle visual modelling analysis, Computers and Mathematics with Applications, 2022-2029, (2009). [Google Scholar]
  7. M.H. Alrefaei, A.H. Diabat. A simulated annealing technique for multi-objective simulation optimization, Applied Mathematics and Computation, 3029-3035, (2009). [Google Scholar]
  8. M. Bahrepour, E. Mahdipour, R. Cheloi, M. Yaghoobi. SUPER SAPSO: A Nex SA-Based PSO Algorithm, Appllications of Soft Computing, 423-430, (2009). [Google Scholar]
  9. W. Du, B. Li. Multi-strategy ensemble particle swarm optimization for dynamic optimization, Information Sciences, 3096-3109, (2008). [Google Scholar]
  10. L. Lamberti. An efficient simulated annealing algorithm for design optimization of truss structures, Computers and Structures, 1936-1953, (2008). [Google Scholar]
  11. S.W. Lin, T.Y. Tseng, S.Y. Chou, S.C. Chen. A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks, Expert Systems with Applications, 1491-1499, (2008). [Google Scholar]
  12. L.L. Li, D.H. Zhou, L. Wang. Fault Diagnosis of Nonlinear Systems based on hybrid PSOSA optimization algorithm, International Journal of Automation and Computing, 183-188, (2007). [Google Scholar]
  13. B. Liu, L. Wang, Y.H. Jin. An effective hybrid particle swarm optimization for no wait flow shop scheduling, Int. J. Adv. Manuf. Technol., 1001-1011, (2007). [Google Scholar]
  14. F. Zhao, Y. Hong, D. Yu, Y. Yang, Q. Zhang, H. Yi. A hybrid algorithm based on particle swarm optimization and simulated annealing to holon task allocation for holonic manufacturing system, Int. J. Adv. Manuf. Technol., 1021-1032, (2007). [Google Scholar]
  15. P.S. Shelokar, P. Siarry, V.K. Jayaraman, B.D. Kulkarni. Particle swarm and ant colony algorithms hybridized for improved continuous optimization, Applied Mathematics and Computation, 129-142, (2007). [Google Scholar]
  16. R. Brits, A.P. Engelbrecht, F. Van Den Bergh. Locating multiple optima using particle swarm optimization, Applied Mathematics and Computation, 1859-1883, (2007). [Google Scholar]
  17. Y. Jiang, T. Hu, C. Huang, X. Wu. An improved particle swarm optimization algorithm, Applied Mathematics and Computation, 231-239, (2007). [Google Scholar]
  18. Y. Liu, Z. Qin, Z. Shi, J. Lu. center particle swarm optimization, Neurocomputing, 672-679, (2007). [Google Scholar]
  19. B. Bochenek, P. Forys. Structural optimization for post buckling behavior using particle swarms, Struct. Multidisc. Optim., 521-531, (2006). [Google Scholar]
  20. D. Chaojin, Q. Zulian. Particle swarm optimization algorithm based on the idea of simulated annealing, International Journal of Computer Science and Network Security 6 (10), (2006). [Google Scholar]
  21. W.J. Xia, Z.M. Wu. A hybrid particle swarm approach for the job shop scheduling problem, Int. J. Adv. Manuf. Technol., 360-366, (2006). [Google Scholar]
  22. R.C. Eberhart, J. Kennedy. A new optimizer using particle swarm theory, Proceedings Sixth Symposium on Micro Machine and Human Science, IEEE Service Center, Piscataway, NJ, 39-43, (1995). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.