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Journal of Control Science and Engineering
Volume 2015 (2015), Article ID 923791, 6 pages
http://dx.doi.org/10.1155/2015/923791
Research Article

Hybrid Particle Swarm and Differential Evolution Algorithm for Solving Multimode Resource-Constrained Project Scheduling Problem

1Guangxi Key Laboratory of New Energy and Building Energy Saving, Guilin University of Technology, Guilin 541004, China
2College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541004, China
3College of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China

Received 14 July 2015; Revised 11 September 2015; Accepted 14 September 2015

Academic Editor: Petko Petkov

Copyright © 2015 Lieping Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. Ballestín and R. Blanco, “Theoretical and practical fundamentals for multi-objective optimisation in resource-constrained project scheduling problems,” Computers and Operations Research, vol. 38, no. 1, pp. 51–62, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  2. P. Ghoddousi, E. Eshtehardian, S. Jooybanpour, and A. Javanmardi, “Multi-mode resource-constrained discrete time-cost-resource optimization in project scheduling using non-dominated sorting genetic algorithm,” Automation in Construction, vol. 30, pp. 216–227, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Brucker, A. Drexl, R. Möhring, K. Neumann, and E. Pesch, “Resource-constrained project scheduling: notation, classification, models, and methods,” European Journal of Operational Research, vol. 112, no. 1, pp. 3–41, 1999. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Jarboui, N. Damak, P. Siarry, and A. Rebai, “A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problems,” Applied Mathematics and Computation, vol. 195, no. 1, pp. 299–308, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  5. V. Van Peteghem and M. Vanhoucke, “Using resource scarceness characteristics to solve the multi-mode resource-constrained project scheduling problem,” Journal of Heuristics, vol. 17, no. 6, pp. 705–728, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. L.-Y. Tseng and S.-C. Chen, “Two-phase genetic local search algorithm for the multimode resource-constrained project scheduling problem,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 4, pp. 848–857, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. N. Damak, B. Jarboui, P. Siarry, and T. Loukil, “Differential evolution for solving multi-mode resource-constrained project scheduling problems,” Computers and Operations Research, vol. 36, no. 9, pp. 2653–2659, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  8. L. Wang and C. Fang, “An effective estimation of distribution algorithm for the multi-mode resource-constrained project scheduling problem,” Computers & Operations Research, vol. 39, no. 2, pp. 449–460, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Li and H. Zhang, “Ant colony optimization-based multi-mode scheduling under renewable and nonrenewable resource constraints,” Automation in Construction, vol. 35, pp. 431–438, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Liu, D. Chen, and Y. Wang, “Memetic algorithm for multi-mode resource-constrained project scheduling problems,” Journal of Systems Engineering and Electronics, vol. 25, no. 4, pp. 609–617, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Liu, Z. Cai, and Y. Wang, “Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization,” Applied Soft Computing Journal, vol. 10, no. 2, pp. 629–640, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. W. Zhang and K. Kang, “Ant colony and particle swarm optimization algorithm-based solution to multi-mode resource-constrained project scheduling problem,” Computer Engineering and Applications, vol. 43, no. 34, pp. 213–216, 2007. View at Google Scholar
  13. R.-M. Chen and F. E. Sandnes, “An efficient particle swarm optimizer with application to man-day project scheduling problems,” Mathematical Problems in Engineering, vol. 2014, Article ID 519414, 9 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Kolisch and A. Sprecher, “PSPLIB—a project scheduling problem library,” European Journal of Operational Research, vol. 96, no. 1, pp. 205–216, 1997. View at Publisher · View at Google Scholar · View at Scopus
  15. A. El-Gallad, M. El-Hawary, A. Sallam, and A. Kalas, “Enhancing the particle swarm optimizer via proper parameters selection,” in Proceedings of the IEEE Canadian Conference on Electrical & Computer Engineering, pp. 792–797, Winnipeg, Canada, May 2002. View at Scopus
  16. Y. H. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proceedings of the Congress on Evolutionary Computation (CEC '99), pp. 1945–1950, Piscataway, NJ, USA, July 1999. View at Publisher · View at Google Scholar · View at Scopus
  17. Z. Huang and Y. Chen, “An improved differential evolution algorithm based on adaptive parameter,” Journal of Control Science and Engineering, vol. 2013, Article ID 462706, 5 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus