Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2017, Article ID 8034573, 10 pages
https://doi.org/10.1155/2017/8034573
Research Article

Firefly Mating Algorithm for Continuous Optimization Problems

1Computational Intelligence Laboratory, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
2Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
3College of Innovative Technology and Engineering, Dhurakij Pundit University, Bangkok, Thailand

Correspondence should be addressed to Arit Thammano; ht.ca.ltimk.ti@tira

Received 15 January 2017; Revised 22 May 2017; Accepted 14 June 2017; Published 20 July 2017

Academic Editor: Leonardo Franco

Copyright © 2017 Amarita Ritthipakdee 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. X.-S. Yang, “Firefly algorithms for multimodal optimization,” in Stochastic Algorithms: Foundations and Applications, vol. 5792 of Lecture Notes in Computer Science, pp. 169–178, Springer, Berlin, Germany, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. A. Baykasoǧlu and F. B. Ozsoydan, “An improved firefly algorithm for solving dynamic multidimensional knapsack problems,” Expert Systems with Applications, vol. 41, no. 8, pp. 3712–3725, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. S. M. Farahani, A. A. Abshouri, B. Nasiri, and M. R. Meybodi, “A gaussian firefly algorithm,” International Journal of Machine Learning and Computing, vol. 1, no. 5, pp. 448–453, 2011. View at Publisher · View at Google Scholar
  4. R. M. Rizk-Allah, E. M. Zaki, and A. A. El-Sawy, “Hybridizing ant colony optimization with firefly algorithm for unconstrained optimization problems,” Applied Mathematics and Computation, vol. 224, pp. 473–483, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  5. Y.-B. Shin and E. Kita, “Search performance improvement of particle swarm optimization by second best particle information,” Applied Mathematics and Computation, vol. 246, pp. 346–354, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. H. Hakli and H. Uǧuz, “A novel particle swarm optimization algorithm with Levy flight,” Applied Soft Computing Journal, vol. 23, pp. 333–345, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Moayedikia, R. Jensen, U. K. Wiil, and R. Forsati, “Weighted bee colony algorithm for discrete optimization problems with application to feature selection,” Engineering Applications of Artificial Intelligence, vol. 44, article no. 2345, pp. 153–167, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. W. F. Gao and S. Y. Liu, “A modified artificial bee colony algorithm,” Computers & Operations Research, vol. 39, pp. 687–697, 2012. View at Publisher · View at Google Scholar
  9. J. Niu, W. Zhong, Y. Liang, N. Luo, and F. Qian, “Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization,” Knowledge-Based Systems, vol. 88, pp. 253–263, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. W.-N. Chen, J. Zhang, Y. Lin et al., “Particle swarm optimization with an aging leader and challengers,” IEEE Transactions on Evolutionary Computation, vol. 17, no. 2, pp. 241–258, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Thakur, “A new genetic algorithm for global optimization of multimodal continuous functions,” Journal of Computational Science, vol. 5, no. 2, pp. 298–311, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. X. S. Yang, Nature-inspired Metaheuristic Algorithm, Luniver Press, Bristol, UK, 2010.
  13. R. De Cock and E. Matthysen, “Sexual communication by pheromones in a firefly, Phosphaenus hemipterus (Coleoptera: Lampyridae),” Animal Behaviour, vol. 70, no. 4, pp. 807–818, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. K. Demary, C. I. Michaelidis, and S. M. Lewis, “Firefly courtship: Behavioral and morphological predictors of male mating success in Photinus greeni,” Ethology, vol. 112, no. 5, pp. 485–492, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. A. South and S. M. Lewis, “Determinants of reproductive success across sequential episodes of sexual selection in a firefly,” Proceedings of the Royal Society B: Biological Sciences, vol. 279, no. 1741, pp. 3201–3208, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. K. C. Demary, “Sperm storage and viability in Photinus fireflies,” Journal of Insect Physiology, vol. 51, no. 7, pp. 837–841, 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. C. L. Fry and G. S. Wilkinson, “Sperm survival in female stalk-eyed flies depends on seminal fluid and meiotic drive,” Evolution, vol. 58, no. 7, pp. 1622–1626, 2004. View at Publisher · View at Google Scholar · View at Scopus
  18. C. S. C. Price, K. A. Dyer, and J. A. Coyne, “Sperm competition between Drosophila males involves both displacement and incapacitation,” Nature, vol. 400, no. 6743, pp. 449–452, 1999. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Li, H. Zhao, X. Weng, and T. Han, “Cognitive behavior optimization algorithm for solving optimization problems,” Applied Soft Computing Journal, vol. 39, pp. 199–222, 2016. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Jie, J. Zhang, H. Zheng, and B. Hou, “Formalized model and analysis of mixed swarm based cooperative particle swarm optimization,” Neurocomputing, vol. 174, pp. 542–552, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Cui, J. Feng, J. Guo, and T. Wang, “A novel single multiplicative neuron model trained by an improved glowworm swarm optimization algorithm for time series prediction,” Knowledge-Based Systems, vol. 88, pp. 195–209, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. X. Jin, Y. Liang, D. Tian, and F. Zhuang, “Particle swarm optimization using dimension selection methods,” Applied Mathematics and Computation, vol. 219, no. 10, pp. 5185–5197, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. F. Zhao, Y. Liu, C. Zhang, and J. Wang, “A self-adaptive harmony PSO search algorithm and its performance analysis,” Expert Systems with Applications, vol. 42, no. 21, pp. 7436–7455, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Shareef, A. A. Ibrahim, and A. H. Mutlag, “Lightning search algorithm,” Applied Soft Computing Journal, vol. 36, pp. 315–333, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. A. J. Umbarkar, M. S. Joshi, and W.-C. Hong, “Multithreaded parallel dual population genetic algorithm (MPDPGA) for unconstrained function optimizations on multi-core system,” Applied Mathematics and Computation, vol. 243, pp. 936–949, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  26. D. Jitkongchuen and A. Thammano, “A self-adaptive differential evolution algorithm for continuous optimization problems,” Artificial Life and Robotics, vol. 19, no. 2, pp. 201–208, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. J. Liu, H. Zhu, Q. Ma, L. Zhang, and H. Xu, “An artificial bee colony algorithm with guide of global & local optima and asynchronous scaling factors for numerical optimization,” Applied Soft Computing Journal, vol. 37, pp. 608–618, 2015. View at Publisher · View at Google Scholar · View at Scopus