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A Hybrid Flower Pollination Algorithm for Engineering Optimization Problems

by Mohamed Abdel-Baset, Ibrahim Hezam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 12
Year of Publication: 2016
Authors: Mohamed Abdel-Baset, Ibrahim Hezam
10.5120/ijca2016909119

Mohamed Abdel-Baset, Ibrahim Hezam . A Hybrid Flower Pollination Algorithm for Engineering Optimization Problems. International Journal of Computer Applications. 140, 12 ( April 2016), 10-23. DOI=10.5120/ijca2016909119

@article{ 10.5120/ijca2016909119,
author = { Mohamed Abdel-Baset, Ibrahim Hezam },
title = { A Hybrid Flower Pollination Algorithm for Engineering Optimization Problems },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 12 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 10-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume140/number12/24644-2016909119/ },
doi = { 10.5120/ijca2016909119 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:42:04.197642+05:30
%A Mohamed Abdel-Baset
%A Ibrahim Hezam
%T A Hybrid Flower Pollination Algorithm for Engineering Optimization Problems
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 12
%P 10-23
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Flower pollination algorithm (FP) is a new nature-inspired algorithm, based on the characteristics of flowering plants. Combining with the features of flower pollination algorithm, an improved simulated annealing algorithm is proposed in this paper (FPSA). It can improve the speed of annealing. The initial state of simulated annealing and new solutions are generated by flower pollination. Therefore, it has the advantage of high quality and efficiency. The method combines the standard flower pollination algorithm (FP) with simulated annealing to enhance the search performance and speeds up the global convergence rate. Structural engineering optimization problems are presented to demonstrate the effectiveness and robustness of the proposed algorithm. The experimental results showed that the accuracy of finding the best solution and convergence speed performance of the proposed algorithm is competitive to those achieved by the existing algorithms.

References
  1. A. Ghodrati and S. Lotfi, “A hybrid CS/PSO algorithm for global optimization,” in Intelligent Information and Database Systems, Springer, 2012, pp. 89–98.
  2. G. Wang, L. Guo, H. Duan, L. Liu, H. Wang, and B. Wang, “A hybrid meta-heuristic DE/CS algorithm for UCAV path planning,” Journal of Information and Computational Science, vol. 5, pp. 4811–4818, 2012.
  3. F. Wang, X. He, L. Luo, and Y. Wang, “Hybrid optimization algorithm of PSO and Cuckoo Search,” in Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on, 2011, pp. 1172–1175.
  4. R. Raju, R. Babukarthik, and P. Dhavachelvan, “Hybrid Ant Colony Optimization and Cuckoo Search Algorithm for Job Scheduling,” in Advances in Computing and Information Technology, Springer, 2013, pp. 491–501.
  5. T. Hassanzadeh and M. R. Meybodi, “A new hybrid algorithm based on Firefly Algorithm and cellular learning automata,” in 20th Iranian Conference on Electrical Engineering (ICEE), 2012, pp. 628–633.
  6. A. Abdullah, S. Deris, M. S. Mohamad, and S. Z. M. Hashim, “A new hybrid firefly algorithm for complex and nonlinear problem,” in Distributed Computing and Artificial Intelligence, Springer, 2012, pp. 673–680.
  7. A. A. El-Sawy, E. M. Zaki, and R. Rizk-Allah, “A Novel Hybrid Ant Colony Optimization and Firefly Algorithm for Solving Constrained Engineering Design Problems,” Journal of Natural Sciences and Mathematics, vol. 6, 2012.
  8. I. M. Hezam and M. Abdel-Baset, “An Improved Flower Pollination Algorithm for Ratios optimization Problems”. Applied Mathematics & Information Sciences Letters An International Journal, vol. 3, No. 2, pp. 83–91, 2015.
  9. S. Farook and P. S. Raju, “Evolutionary Hybrid Genetic-Firefly Algorithm for Global Optimization.”
  10. 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.
  11. O. Abdel-Raouf, M. Abdel-Baset, and I. El-henawy, “An Improved Flower Pollination Algorithm with Chaos,” I.J. Education and Management Engineering, vol. 2, pp. 1–8, 2014.
  12. V. M. Gellat CD Kirkpatrick S, “Optimization by simulated annealing,” Science, vol. 220(4598):6, pp. 71–80, 1983.
  13. A. Askarzadeh, “A discrete chaotic harmony search-based simulated annealing algorithm for optimum design of PV/wind hybrid system,” Solar Energy, vol. 97, pp. 93–101, 2013.
  14. S. C. Leung, D. Zhang, C. Zhou, and T. Wu, “A hybrid simulated annealing metaheuristic algorithm for the two-dimensional knapsack packing problem,” Computers & Operations Research, vol. 39, pp. 64–73, 2012.
  15. S. M. Mousavi and R. Tavakkoli-Moghaddam, “A hybrid simulated annealing algorithm for location and routing scheduling problems with cross-docking in the supply chain,” Journal of Manufacturing Systems, vol. 32, pp. 335–347, 2013.
  16. X.-S. Yang, Nature-inspired metaheuristic algorithms. Luniver Press, 2011.
  17. K. Deb, “An efficient constraint handling method for genetic algorithms,” Computer methods in applied mechanics and engineering, vol. 186, pp. 311–338, 2000.
  18. L. C. Cagnina, S. C. Esquivel, and C. A. C. Coello, “Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer.,” Informatica (Slovenia), vol. 32, pp. 319–326, 2008.
  19. H. Eskandar, A. Sadollah, A. Bahreininejad, and M. Hamdi, “Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems,” Computers & Structures, vol. 110, pp. 151–166, 2012.
  20. M. Jaberipour and E. Khorram, “Two improved harmony search algorithms for solving engineering optimization problems,” Communications in Nonlinear Science and Numerical Simulation, vol. 15, pp. 3316–3331, 2010.
  21. A. Sadollah, A. Bahreininejad, H. Eskandar, and M. Hamdi, “Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems,” Applied Soft Computing, vol. 13, pp. 2592–2612, 2013.
  22. X.-S. Yang, Engineering optimization: an introduction with metaheuristic applications. Wiley, 2010.
  23. X.-S. Yang and A. Hossein Gandomi, “Bat algorithm: a novel approach for global engineering optimization,” Engineering Computations, vol. 29, pp. 464–483, 2012.
  24. M.-H. Lin, J.-F. Tsai, N.-Z. Hu, and S.-C. Chang, “Design Optimization of a Speed Reducer Using Deterministic Techniques,” Mathematical Problems in Engineering, vol. 2013, 2013.
  25. O. Abdel-Raouf, I. El-henawy and M. Abdel-Baset "chaotic Harmony Search Algorithm with Different Chaotic Maps for Solving Assignment Problems "International Journal of Computational Engineering & Management, Vol. 17, pp. 10-15 ,2014.
  26. X.-S. Yang and S. Deb, “Cuckoo search via Lévy flights,” in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, 2009, pp. 210–214.
  27. A. H. Gandomi, X.-S. Yang, and A. H. Alavi, “Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems,” Engineering with Computers, vol. 29, pp. 17–35, 2013.
  28. O. A. Raouf, M. A. Baset, I. M. Elhenawy, “A New Hybrid Flower Pollination Algorithm for Solving Constrained Global Optimization Problems”, International Journal of Applied Operational Research Vol. 4, No. 2, pp. 1-13, Spring 2014.
  29. D. Maiti, S. Biswas, and A. Konar, “Design of a fractional order PID controller using particle swarm optimization technique,” arXiv preprint arXiv:0810.3776, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Flower pollination simulated annealing algorithm constrained optimization problems engineering optimization problems global optimization.

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