Open Access
Issue
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 03039
Number of page(s) 7
Section Algorithm Study and Mathematical Application
DOI https://doi.org/10.1051/matecconf/201823203039
Published online 19 November 2018
  1. Jones, D. F., Mirrazavi, S. K., & Tamiz, M. Multi-objective meta-heuristics: An overview of the current state-of-the-art. European Journal of Operational Research, 137, 1-9. (2002). [CrossRef] [Google Scholar]
  2. Kennedy J., Eberhart R. “Particle Swarm Optimization”. Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948. (1995). [CrossRef] [Google Scholar]
  3. Dang, D. C., Guibadj, R. N., & Moukrim, A. An effective PSO-inspired algorithm for the team orienteering problem. European Journal of Operational Research, 229, 332–344. (2013). [CrossRef] [Google Scholar]
  4. Nayeri, P., Yang, F., & Elsherbeni, A. Z. Design of single-feed reflectarray antennas with asymmetric multiple beams using the particle swarm optimization method. IEEE Transactions on Antennas and Propagation, 61, 4598–4605. (2013). [CrossRef] [Google Scholar]
  5. Nebro, A. J., Durillo, J. J., Garcia-Nieto, J., Coello Coello, C. A., Luna, F., & Alba, E. SMPSO: A new PSO-based metaheuristic for multi-objective optimization. In Proceedings of IEEE symposium on computational intelligence in multi-criteria decision-making, pp. 66–73. (2009). [Google Scholar]
  6. Martinez, S. Z., & Coello Coello, C. A. A multi-objective particle swarm optimizer based on decomposition. In Proceedings of the 13th annual genetic and evolutionary computation conference, pp. 69–76. (2011). [Google Scholar]
  7. Q. Lin, J. Li, Z. Du, J Chen, Z. Ming. “A novel multi-objective particle swarm optimization with multiple search strategies,” Eur. J. Oper. Res., vol.247, no. 3, pp732-744 (2015). [CrossRef] [Google Scholar]
  8. Gheorghe Păun, Grzegorz Rozenberg. A guide to membrane computing[J].Theoretical Computer Science. 287:73-100,(2002) [CrossRef] [Google Scholar]
  9. Dou Zengfa, Gao Lin. Feature selection in conditional random fields using a membrane particle swarm optimizer. Journal of XIDIAN university, vol.39 no.5 107-112, (2012). [Google Scholar]
  10. ZHANG Qunhui, LI Renfa. Cloud resource scheduling based On improved particle swarm optimization algorithm by membranecomputing. Computer Engineeringand Applications, 49(20):40-44. (2013) [Google Scholar]
  11. X. D. Li, “A non-dominated sorting particle swarm optimizer for multiobjective optimization,” in Proc. Genet. Evol. Comput., vol. LNCS 2723, pp. 37-48. (2003). [Google Scholar]
  12. D. Srinivasan and T. H. Seow, “Particle swarm inspired evolutionary algorithm (PS-EA) for multiobjective optimization problem,” in Proc. Congr. Evol. Comput., pp. 2292–2297. (2003) [Google Scholar]
  13. Martín-Vide, C., Paun, G., Pazos, J. and Rodríguez-Patón, Alfonso. Tissue P systems. Theor. Comput. Sci. 296 (2003), 295-326. [CrossRef] [Google Scholar]
  14. Freund, R., Păun, G. and Pérez-Jiménez, M.J. Tissue P systems with channel states. Theor. Comput. Sci. 330 (2005), 101-116. [CrossRef] [Google Scholar]
  15. Leporati, A.,Zandron, C.,Ferretti, C. and Mauri, G. On the computational power of spiking neural P systems. Int. J. Unconv. Comput 5 (2009), 459–473. [Google Scholar]
  16. García-Arnau, M.,Pérez, D.,Rodríguez-Patón, A. and Sosík, P. Spiking neural P systems: stronger normal forms. Int. J. Unconv. Comput 5 (2009), 411–425. [Google Scholar]
  17. Timothy Ganesan,Pandian Vasant,Irraivan Elamvazuthy. A hybrid PSO approach for solving non-convex optimization problems[J]. Archives of Control Sciences,(2012),22(1). [Google Scholar]
  18. Q. Zhang and H. Li, “MOEA/D: A multiobjective evolutionary algorithm based on decomposition,” IEEE Trans. Evol. Comput., (2007).vol. 11, pp. 712–731, Dec. [CrossRef] [Google Scholar]
  19. E. Zitzler, M. Laumanns, and L. Thiele, SPEA2: Improving the strength Pareto evolutionaryalgorithm Comput. Eng. Networks Lab., Swiss Fed. Inst. Technol., Zurich, Switzerland, (2001), Tech. Rep. 103. [Google Scholar]
  20. J. D. Knowles and D. W. Corne, “Approximating the nondominated front using the Pareto archivedevolution strategy,” Evol. Comput., (2000),vol. 8, no. 2, pp. 149-172. [CrossRef] [Google Scholar]
  21. K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm:NSGA-II, IEEE Trans. Evol. Comput., (2002).vol. 6,pp. 182–197, Apr. [CrossRef] [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.