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Discrete Dynamics in Nature and Society
Volume 2017 (2017), Article ID 8342694, 23 pages
https://doi.org/10.1155/2017/8342694
Research Article

A Hybrid Lightning Search Algorithm-Simplex Method for Global Optimization

1School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
2College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China
3Key Laboratory of Guangxi High Schools Complex System and Intelligent Computing, Nanning 530006, China

Correspondence should be addressed to Yongquan Zhou

Received 11 March 2017; Accepted 1 June 2017; Published 13 July 2017

Academic Editor: Pasquale Candito

Copyright © 2017 Yuting Lu 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. T. Weise, M. Zapf, R. Chiong, and A. J. Nebro, “Why is optimization difficult,” in Nature-Inspired Algorithms for Optimisation, Studies in Computational Intelligence, vol. 193, pp. 1–50, Springer, Berlin, Germany, 2009. View at Google Scholar
  2. J. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, USA, 1975.
  3. M. Dorigo, Optimization, Learning and Natural Algorithms [Ph.D. thesis], Politecnico di Milano, Milan, Italy, 1992.
  4. J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Piscataway, NJ, USA, 1995.
  5. Z. W. Geem, J. H. Kim, and G. V. Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 687–697, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. X. S. Yang and S. Deb, “Cuckoo search via Lévy flights,” in Proceedings of the World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214, IEEE Publications, 2009.
  8. X.-S. Yang, “A new metaheuristic bat-inspired algorithm,” in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), J. R. Gonzalez, D. A. Pelta, C. Cruz, G. Terrazas, and N. Krasnogor, Eds., vol. 284 of Studies in Computational Intelligence, pp. 65–74, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  9. K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Systems Magazine, vol. 22, no. 3, pp. 52–67, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Hatamlou, “Black hole: a new heuristic optimization approach for data clustering,” Information Sciences. An International Journal, vol. 222, pp. 175–184, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. 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
  12. H. Shareef, A. H. Mutlag, and A. Mohamed, “A novel approach for fuzzy logic PV inverter controller optimization using lightning search algorithm,” Neurocomputing, vol. 168, pp. 435–453, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. J. A. Ali, M. A. Hannan, and A. Mohamed, “A novel quantum-behaved lightning search algorithm approach to improve the fuzzy logic speed controller for an induction motor drive,” Energies, vol. 8, no. 11, pp. 13112–13136, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. M. M. Islam, H. Shareef, A. Mohamed, and A. Wahyudie, “A binary variant of lightning search algorithm: BLSA,” Soft Computing, pp. 1–20, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Sirjani and H. Shareef, “Parameter extraction of solar cell models using the lightning search algorithm in different weather conditions,” Journal of Solar Energy Engineering, vol. 138, no. 4, 2016. View at Publisher · View at Google Scholar · View at Scopus
  16. A. H. Mutlag, A. Mohamed, and H. Shareef, “A nature-inspired optimization-based optimum fuzzy logic photovoltaic inverter controller utilizing an eZdsp F28335 board,” Energies, vol. 9, no. 3, article 120, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. M. S. Ahmed, A. Mohamed, R. Z. Homod, and H. Shareef, “Hybrid LSA-ANN based home energy management scheduling controller for residential demand response strategy,” Energies, vol. 9, no. 9, article 716, 2016. View at Publisher · View at Google Scholar
  18. H. Faris, I. Aljarah, N. Al-Madi, and S. Mirjalili, “Optimizing the learning process of feedforward neural networks using lightning search algorithm,” International Journal on Artificial Intelligence Tools, vol. 25, no. 6, article 1650033, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. C. A. C. Coello, “Use of a self-adaptive penalty approach for engineering optimization problems,” Computers in Industry, vol. 41, no. 2, pp. 113–127, 2000. View at Publisher · View at Google Scholar · View at Scopus
  20. C. A. C. Coello and E. M. Montes, “Constraint-handling in genetic algorithms through the use of dominance-based tournament selection,” Advanced Engineering Informatics, vol. 16, no. 3, pp. 193–203, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. E. Mezura-Montes and C. A. Coello Coello, “An empirical study about the usefulness of evolution strategies to solve constrained optimization problems,” International Journal of General Systems, vol. 37, no. 4, pp. 443–473, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. K. E. Parsopoulos and M. N. Vrahatis, “Unified particle swarm optimization for solving constrained engineering optimization problems,” in Advances in Natural Computation, vol. 3612 of Lecture Notes in Computer Science, pp. 582–591, Springer, Berlin, Germany, 2005. View at Publisher · View at Google Scholar
  23. Q. He and L. Wang, “An effective co-evolutionary particle swarm optimization for constrained engineering design problems,” Engineering Applications of Artificial Intelligence, vol. 20, no. 1, pp. 89–99, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. F. Z. Huang, L. Wang, and Q. He, “An effective co-evolutionary differential evolution for constrained optimization,” Applied Mathematics and Computation, vol. 186, no. 1, pp. 340–356, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  25. M. Mahdavi, M. Fesanghary, and E. Damangir, “An improved harmony search algorithm for solving optimization problems,” Applied Mathematics and Computation, vol. 188, no. 2, pp. 1567–1579, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  26. B. Akay and D. Karaboga, “Artificial bee colony algorithm for large-scale problems and engineering design optimization,” Journal of Intelligent Manufacturing, vol. 23, no. 4, pp. 1001–1014, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Mirjalili, “Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm,” Knowledge-Based Systems, vol. 89, pp. 228–249, 2015. View at Publisher · View at Google Scholar · View at Scopus
  28. S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Baykasoğlu and F. B. Ozsoydan, “Adaptive firefly algorithm with chaos for mechanical design optimization problems,” Applied Soft Computing, vol. 36, pp. 152–164, 2015. View at Publisher · View at Google Scholar
  30. A. H. Gandomi, X. S. Yang, A. H. Alavi, and S. Talatahari, “Bat algorithm for constrained optimization tasks,” Neural Computing and Applications, vol. 22, no. 6, pp. 1239–1255, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. 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, no. 1, pp. 17–35, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. H. Chickermane and H. C. Gea, “Structural optimization using a new local approximation method,” International Journal for Numerical Methods in Engineering, vol. 39, no. 5, pp. 829–846, 1996. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  33. M.-Y. Cheng and D. Prayogo, “Symbiotic organisms search: a new metaheuristic optimization algorithm,” Computers & Structures, vol. 139, pp. 98–112, 2014. View at Publisher · View at Google Scholar · View at Scopus
  34. A. Kaveh and S. Talatahari, “An improved ant colony optimization for constrained engineering design problems,” Engineering Computations, vol. 27, no. 1, pp. 155–182, 2010. View at Publisher · View at Google Scholar · View at Scopus
  35. R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303–315, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. K. Deb, “Optimal design of a welded beam via genetic algorithms,” AIAA Journal, vol. 29, no. 11, pp. 2013–2015, 1991. View at Publisher · View at Google Scholar
  37. A. Kaveh and S. Talatahari, “A novel heuristic optimization method: charged system search,” Acta Mechanica, vol. 213, no. 3-4, pp. 267–289, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. J.-F. Tsai, “Global optimization of nonlinear fractional programming problems in engineering design,” Engineering Optimization, vol. 37, no. 4, pp. 399–409, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  39. T. Ray and P. Saini, “Engineering design optimization using a swarm with an intelligent information sharing among individuals,” Engineering Optimization, vol. 33, no. 6, pp. 735–748, 2001. View at Publisher · View at Google Scholar · View at Scopus
  40. T. Ray and K. M. Liew, “Society and civilization: an optimization algorithm based on the simulation of social behavior,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 4, pp. 386–396, 2003. View at Publisher · View at Google Scholar · View at Scopus
  41. M. Zhang, W. Luo, and X. Wang, “Differential evolution with dynamic stochastic selection for constrained optimization,” Information Sciences, vol. 178, no. 15, pp. 3043–3074, 2008. View at Publisher · View at Google Scholar · View at Scopus
  42. 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
  43. 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, no. 5, pp. 2592–2612, 2013. View at Publisher · View at Google Scholar · View at Scopus
  44. E. Davoodi, M. T. Hagh, and S. G. Zadeh, “A hybrid improved quantum-behaved particle swarm optimization-simplex method (IQPSOS) to solve power system load flow problems,” Applied Soft Computing Journal, vol. 21, pp. 171–179, 2014. View at Publisher · View at Google Scholar · View at Scopus
  45. A. S. El-Wakeel, “Design optimization of PM couplings using hybrid particle swarm optimization-simplex method (PSO-SM) algorithm,” Electric Power Systems Research, vol. 116, pp. 29–35, 2014. View at Publisher · View at Google Scholar · View at Scopus
  46. H. R. Tizhoosh, “Opposition-based learning: a new scheme for machine intelligence,” in Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation, International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA/IAWTIC '05), vol. 1, pp. 695–701, Vienna, Austria, 2005. View at Publisher · View at Google Scholar
  47. H. Wang, Z. Wu, and S. Rahnamayan, “Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems,” Soft Computing, vol. 15, no. 11, pp. 2127–2140, 2011. View at Publisher · View at Google Scholar · View at Scopus
  48. Y. Zhou, R. Wang, and Q. Luo, “Elite opposition-based flower pollination algorithm,” Neurocomputing, vol. 188, pp. 294–310, 2016. View at Publisher · View at Google Scholar · View at Scopus
  49. X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 82–102, 1999. View at Publisher · View at Google Scholar · View at Scopus
  50. X.-S. Yang, “Test problems in optimization,” in Engineering Optimization: An Introduction with Metaheuristic Applications, X.-S. Yang, Ed., John Wiley & Sons, 2010. View at Google Scholar
  51. E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: a gravitational search algorithm,” Information Sciences, vol. 213, pp. 267–289, 2010. View at Publisher · View at Google Scholar · View at Scopus
  52. X. S. Yang, “Flower pollination algorithm for global optimization,” in Unconventional Computation and Natural Computation, vol. 7445 of Lecture Notes in Computer Science, pp. 240–249, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  53. S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, 2016. View at Publisher · View at Google Scholar · View at Scopus
  54. S. Saremi, S. Mirjalili, and A. Lewis, “Grasshopper optimisation algorithm: theory and application,” Advances in Engineering Software, vol. 105, pp. 30–47, 2017. View at Publisher · View at Google Scholar
  55. M. Abdullahi, M. A. Ngadi, and S. M. Abdulhamid, “Symbiotic Organism Search optimization based task scheduling in cloud computing environment,” Future Generation Computer Systems, vol. 56, pp. 640–650, 2016. View at Publisher · View at Google Scholar · View at Scopus