Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper

-

May Edition 2020

International Journal of Applied Information Systems solicits high quality original research papers for the May 2020 Edition of the journal. The last date of research paper submission is April 15, 2020.

A Comparative Study of Flower Pollination Algorithm and Bat Algorithm on Continuous Optimization Problems

Nazmus Sakib, Md. Wasi Ul Kabir, Md Subbir Rahman, Md Shafiul Alam Published in Algorithms

International Journal of Applied Information Systems
Year of Publication: 2014
© 2013 by IJAIS Journal
10.5120/ijais14-451231
Download full text
  1. Nazmus Sakib, Md. Wasi Ul Kabir, Md Subbir Rahman and Md Shafiul Alam. Article: A Comparative Study of Flower Pollination Algorithm and Bat Algorithm on Continuous Optimization Problems. International Journal of Applied Information Systems 7(9):20-19, September 2014. BibTeX

    @article{key:article,
    	author = "Nazmus Sakib and Md. Wasi Ul Kabir and Md Subbir Rahman and Md Shafiul Alam",
    	title = "Article: A Comparative Study of Flower Pollination Algorithm and Bat Algorithm on Continuous Optimization Problems",
    	journal = "International Journal of Applied Information Systems",
    	year = 2014,
    	volume = 7,
    	number = 9,
    	pages = "20-19",
    	month = "September",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Nature is a rich source of inspiration, which has inspired many researchers in many ways. Nowadays, new algorithms have been developed by the inspiration from nature. The flower pollination algorithm is based on the characteristics of pollination process of flowers plants. Pollination is a natural biological process of mating in plants. In flowers, pollen is carried to stigma through some mechanisms that confirm a proper balance in the genetic creations of the species. Another nature inspired algorithm — the Bat algorithm is based on the echolocation behavior of bats. In this paper, the Flower pollination algorithm is compared with the basic Bat algorithm. We have tested these two algorithms on both unimodal and multimodal, low and high dimensional continuous functions. Simulation results suggest that the Flower pollination algorithm can perform much better than the Bat algorithm on the continuous optimization problems.

Reference

  1. Y. Xin-She, Engineering Optimization: An Introduction with Metaheuristic Application, Wiley, 2010.
  2. H. A. Abbass and R. Sarker, "The Pareto di?ential evolution algorithm," Int. J. Arti?cial Intelligence Tools, vol. 11(4), pp. 531-552, 2002.
  3. Y. Xin-She, "Nature-inspired Metaheuristic Algorithms," Luniver Press, 2008.
  4. K. Deb, Multi-Objective optimization using evolutionary algorithms, New York: John Wiley & Sons, 2001.
  5. Y. Xin-She, K. Mehmet and H. Xingshi, "Multi-objective Flower Algorithm for Optimization," in International Conference on Computational Science, ICCS 2013, 2013.
  6. M. Walker, "How ?owers conquered the world," BBC Earth News, 10 July 2009.
  7. Y. Xin-She, "Flower pollination algorithm for global optimization," Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, vol. 7445, p. 240–249, 2012.
  8. G. Wang and L. Guo, "A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization," Journal of Applied Mathematics, vol. 2013, p. 21, 2013.
  9. Y. Xin-She, "A New Metaheuristic Bat-Inspired Algorithm, Nature Inspired Cooperative Strategies for Optimization (NISCO 2010)," Springer, vol. 284, no. Springer Berlin,, pp. 65-74 , 2010.
  10. K. Khan and A. Sahai, "A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-Learning Context," I. J. Intelligent Systems and Applications, pp. 23-29, June 2012.
  11. N. S. S. M. M. S. A. Md. Wasi Ul Kabir, "A Novel Adaptive Bat Algorithm to Control Explorations nd Exploitations for Continuous Optimization Problems," International Journal of Computer Applications , vol. 94, no. 13, 2014.
  12. A Rekaby, "Directed Artificial Bat Algorithm (DABA) A New Bio-Inspired Algorithm," in International Conference on Advances in Computing, Communications and Informatics (ICACCI), Cairo, 2013.
  13. Y. Selim and U. K. Ecir, "Improved Bat Algorithm (IBA) on Continuous Optimization Problems," Lecture Notes on Software Engineering, vol. 1, no. 3, pp. 279-283, 2013.
  14. G. Q. Huang, W. J. Zhao and Q. Q. Lu, "Bat algorithm with global convergence for solving large scale optimization problem," Application Research of Computers , vol. 30, no. 3, pp. 1-10, 2013.
  15. K. Gaganpreet and D. S. Dr. , "Pollination Based Optimization or Color Image Segmentation," International Journal of Computer Engineering and Technology (IJCET), vol. 3, no. 2, pp. 407-414, July-September 2012.
  16. K. S, "Pollination based optimization," in 6th International Multi Conference on Intelligent Systems, Sustainable, New and Renewable Energy Technology and Nanotechnology (IISN2012), March 16-18,2012.
  17. N. Waser, "Flower constancy: de?nition, cause and measurement," The American Naturalist, 1986.
  18. A. -R. Osama, A. -B. Mohamed and E. -h. Ibrahim, "A Novel Hybrid Flower Pollination Algorithm with Chaotic Harmony Search for Solving Sudoku Puzzles," International Journal of Engineering Trends and Technology (IJETT), vol. 7, no. 3, pp. 126-132, January 2014.
  19. J. Altringham, Bats: Biology and Behaviour, Oxford University Press, 1996.
  20. T. Colin, The Varienty of Life, Oxford University Press, 2000.
  21. A. Faritha and C. Chandrasekar, "An optimized approach of modified bat algorithm to record deduplication," International Journal of Computer Applications, vol. 62, no. 1, pp. 10-15, 2012.
  22. Y. Xin-She, "Bat Algorithm for Multiobjective Optimization," International Journal Bio-Inspired Computation, vol. 3, no. 5, pp. 267-274, 2011.
  23. X. S. Yang, "Harmony Search as a Metaheuristic Algorithm,Music-Inspired Harmony Search Algorithm," Theory and Applications,Studies in Computational Intelligence, vol. 191, pp. 1-14, 2009.
  24. X. S. Yang and J. R. Gonzalez, ""A New Metaheuristic Bat-Inspired Algorithm" in Nature Inspired Cooperative Strategies for Optimization (NISCO 2010)," Springer Press, vol. 284, pp. 65-74, 2010.
  25. S. Yang X, "Nature-inspired Metaheuristic Algorithms," Luniver Press, 2008.
  26. Y. Selim and K. E. U. , "Improved Bat Algorithm (IBA) on Continuous Optimization Problems," Lecture Notes on Software Engineering, vol. 1, no. 3, pp. 279-283, August 2013.
  27. K. Dervis and O. Celal, "A novel clustering approach: Artificial Bee Colony (ABC) algorithm," Applied Soft Computing, Elsevier, vol. 11, pp. 652-657, 2011.
  28. S. Rainer and P. Kenneth, "Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces," Journal of Global Optimization, Kluwer Academic Publishers, pp. 341-357, 1997.
  29. R. Poli, J. Kennedy and T. Blackwell, "Particle Swarm Optimization," Springer Science , vol. 1, pp. 33-57, 1 August 2007.

Keywords

Flower pollination algorithm, Bat algorithm, Swarm intelligence, Meta-heuristic optimization.