The Scientific World Journal
Volume 2013 (2013), Article ID 125625, 9 pages
http://dx.doi.org/10.1155/2013/125625
Research Article
An Effective Hybrid Firefly Algorithm with Harmony Search for Global Numerical Optimization
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu 221116, China
Received 10 August 2013; Accepted 29 September 2013
Academic Editors: Z. Cui and X. Yang
Copyright © 2013 Lihong Guo 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
- G. Wang and L. Guo, “A novel hybrid bat algorithm with harmony search for global numerical optimization,” Journal of Applied Mathematics, vol. 2013, Article ID 696491, 21 pages, 2013. View at Publisher · View at Google Scholar
- X. Li and M. Yin, “An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure,” Advances in Engineering Software, vol. 55, pp. 10–31, 2013. View at Google Scholar
- D. Zou, L. Gao, S. Li, and J. Wu, “An effective global harmony search algorithm for reliability problems,” Expert Systems with Applications, vol. 38, no. 4, pp. 4642–4648, 2011. View at Publisher · View at Google Scholar · View at Scopus
- D. Zou, L. Gao, J. Wu, S. Li, and Y. Li, “A novel global harmony search algorithm for reliability problems,” Computers and Industrial Engineering, vol. 58, no. 2, pp. 307–316, 2010. View at Publisher · View at Google Scholar · View at Scopus
- X.-S. Yang, Z. Cui, R. Xiao, A. H. Gandomi, and M. Karamanoglu, Swarm Intelligence and Bio-Inspired Computation, Elsevier, Waltham, Mass, USA, 2013.
- A. H. Gandomi, X. S. Yang, S. Talatahari, and A. H. Alavi, Metaheuristic Applications in Structures and Infrastructures, Elsevier, Waltham, Mass, USA, 2013.
- X. S. Yang, A. H. Gandomi, S. Talatahari, and A. H. Alavi, Metaheuristics in Water, Geotechnical and Transport Engineering, Elsevier, Waltham, Mass, USA, 2013.
- D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Boston, Mass, USA, 1989.
- T. Back, Evolutionary Algorithms in Theory and Practice, Oxford University Press, Oxford, UK, 1996.
- H. Beyer, The Theory of Evolution Strategies, Springer, New York, NY, USA, 2001.
- M. Dorigo and T. Stutzle, Ant Colony Optimization, MIT Press, Cambridge, UK, 2004.
- B. Shumeet, “Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning,” Carnegie Mellon University CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, Pa, USA, 1994. View at Google Scholar
- O. K. Erol and I. Eksin, “A new optimization method: big bang-big crunch,” Advances in Engineering Software, vol. 37, no. 2, pp. 106–111, 2006. View at Publisher · View at Google Scholar · View at Scopus
- A. Kaveh and S. Talatahari, “Size optimization of space trusses using big bang-big crunch algorithm,” Computers and Structures, vol. 87, no. 17-18, pp. 1129–1140, 2009. View at Publisher · View at Google Scholar · View at Scopus
- A. Kaveh and S. Talatahari, “Optimal design of schwedler and ribbed domes via hybrid big bang-big crunch algorithm,” Journal of Constructional Steel Research, vol. 66, no. 3, pp. 412–419, 2010. View at Publisher · View at Google Scholar · View at Scopus
- A. Kaveh and S. Talatahari, “A discrete big bang-big crunch algorithm for optimal design of skeletal structures,” Asian Journal of Civil Engineering, vol. 11, no. 1, pp. 103–122, 2010. View at Google Scholar · View at Scopus
- 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 Google Scholar · View at Scopus
- P. Yadav, R. Kumar, S. K. Panda, and C. S. Chang, “An intelligent tuned harmony search algorithm for optimisation,” Information Sciences, vol. 196, pp. 47–72, 2012. View at Publisher · View at Google Scholar · View at Scopus
- S. Gholizadeh and A. Barzegar, “Shape optimization of structures for frequency constraints by sequential harmony search algorithm,” Engineering Optimization, vol. 45, no. 6, pp. 627–646, 2013. View at Google Scholar
- 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
- L. Xie, J. Zeng, and R. A. Formato, “Selection strategies for gravitational constant G in artificial physics optimisation based on analysis of convergence properties,” International Journal of Bio-Inspired Computation, vol. 4, no. 6, pp. 380–391, 2012. View at Google Scholar
- A. H. Gandomi, X.-S. Yang, A. H. Alavi, and S. Talatahari, “Bat algorithm for constrained optimization tasks,” Neural Computing & Applications, vol. 22, no. 6, pp. 1239–1255, 2013. View at Google Scholar
- X. S. Yang and A. H. Gandomi, “Bat algorithm: a novel approach for global engineering optimization,” Engineering Computations, vol. 29, no. 5, pp. 464–483, 2012. View at Google Scholar
- X. Li, J. Zhang, and M. Yin, “Animal migration optimization: an optimization algorithm inspired by animal migration behavior,” Neural Computing and Applications, 2013. View at Publisher · View at Google Scholar
- A. H. Gandomi and A. H. Alavi, “Krill herd: a new bio-inspired optimization algorithm,” Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 12, pp. 4831–4845, 2012. View at Google Scholar
- G.-G. Wang, A. H. Gandomi, and A. H. Alavi, “Stud krill herd algorithm,” Neurocomputing, 2013. View at Publisher · View at Google Scholar
- G.-G. Wang, A. H. Gandomi, and A. H. Alavi, “An effective krill herd algorithm with migration operator in biogeography-based optimization,” Applied Mathematical Modelling, 2013. View at Publisher · View at Google Scholar
- R. Storn and K. Price, “Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces,” Tech. Rep. 1075-4946, International Computer Science Institute, Berkley, Calif, USA, 1995. View at Google Scholar
- R. Storn and K. Price, “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Google Scholar · View at Scopus
- X. Li and M. Yin, “Application of differential evolution algorithm on self-potential data,” PLoS One, vol. 7, no. 12, Article ID e51199, 2012. View at Publisher · View at Google Scholar
- G. G. Wang, A. H. Gandomi, A. H. Alavi, and G. S. Hao, “Hybrid krill herd algorithm with differential evolution for global numerical optimization,” Neural Computing & Applications, 2013. View at Publisher · View at Google Scholar
- J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, Australia, December 1995. View at Scopus
- R. J. Kuo, Y. J. Syu, Z.-Y. Chen, and F. C. Tien, “Integration of particle swarm optimization and genetic algorithm for dynamic clustering,” Information Sciences, vol. 195, pp. 124–140, 2012. View at Publisher · View at Google Scholar · View at Scopus
- S. Talatahari, M. Kheirollahi, C. Farahmandpour, and A. H. Gandomi, “A multi-stage particle swarm for optimum design of truss structures,” Neural Computing & Applications, vol. 23, no. 5, pp. 1297–1309, 2013. View at Google Scholar
- K. Y. Huang, “A hybrid particle swarm optimization approach for clustering and classification of datasets,” Knowledge-Based Systems, vol. 24, no. 3, pp. 420–426, 2011. View at Publisher · View at Google Scholar · View at Scopus
- W. Khatib and P. Fleming, “The stud GA: a mini revolution?” Parallel Problem Solving from Nature, pp. 683–691, 1998. View at Google Scholar
- X. S. Yang and S. Deb, “Cuckoo search via Lévy flights,” in Proceedings of the World Congress on Nature and Biologically Inspired Computing (NABIC '09), pp. 210–214, Coimbatore, India, December 2009. View at Publisher · View at Google Scholar · View at Scopus
- A. H. Gandomi, S. Talatahari, X. S. Yang, and S. Deb, “Design optimization of truss structures using cuckoo search algorithm,” The Structural Design of Tall and Special Buildings, vol. 22, no. 17, pp. 1330–1349, 2013. View at Publisher · View at Google Scholar
- X. Cai, S. Fan, and Y. Tan, “Light responsive curve selection for photosynthesis operator of APOA,” International Journal of Bio-Inspired Computation, vol. 4, no. 6, pp. 373–379, 2012. View at Google Scholar
- D. Simon, “Biogeography-based optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702–713, 2008. View at Publisher · View at Google Scholar · View at Scopus
- A. H. Gandomi, X.-S. Yang, and A. H. Alavi, “Mixed variable structural optimization using firefly algorithm,” Computers & Structures, vol. 89, no. 23-24, pp. 2325–2336, 2011. View at Publisher · View at Google Scholar · View at Scopus
- X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver, Frome, UK, 2008.
- X. S. Yang, “Firefly algorithms for multimodal optimization,” in Proceedings of the 5th International Conference on Stochastic Algorithms: Foundations and Applications, pp. 169–178, Springer, Sapporo, Japan, 2009.
- X. S. Yang, “Firefly algorithm, stochastic test functions and design optimisation,” International Journal of Bio-Inspired Computation, vol. 2, no. 2, pp. 78–84, 2010. View at Google Scholar
- X.-S. Yang, S. S. S. Hosseini, and A. H. Gandomi, “Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect,” Applied Soft Computing Journal, vol. 12, no. 3, pp. 1180–1186, 2012. View at Publisher · View at Google Scholar · View at Scopus
- R. Parpinelli and H. Lopes, “New inspirations in swarm intelligence: a survey,” International Journal of Bio-Inspired Computation, vol. 3, no. 1, pp. 1–16, 2011. View at Google Scholar
- D. Zou, L. Gao, J. Wu, and S. Li, “Novel global harmony search algorithm for unconstrained problems,” Neurocomputing, vol. 73, no. 16–18, pp. 3308–3318, 2010. View at Publisher · View at Google Scholar · View at Scopus
- G. Wang, L. Guo, H. Wang, H. Duan, L. Liu, and J. Li, “Incorporating mutation scheme into krill herd algorithm for global numerical optimization,” Neural Computing and Applications, 2012. View at Publisher · View at Google Scholar
- S. Z. Zhao, P. N. Suganthan, Q.-K. Pan, and M. Fatih Tasgetiren, “Dynamic multi-swarm particle swarm optimizer with harmony search,” Expert Systems with Applications, vol. 38, no. 4, pp. 3735–3742, 2011. View at Publisher · View at Google Scholar · View at Scopus
- G. Wang, L. Guo, H. Duan, L. Liu, and H. Wang, “A modified firefly algorithm for UCAV path planning,” International Journal of Hybrid Information Technology, vol. 5, no. 3, pp. 123–144, 2012. View at Google Scholar
- A. H. Gandomi, X. S. Yang, S. Talatahari, and A. H. Alavi, “Firefly algorithm with chaos,” Communications in Nonlinear Science and Numerical Simulation, vol. 18, no. 1, pp. 89–98, 2013. View at Google Scholar
- Y. Zhang, D. Huang, M. Ji, and F. Xie, “Image segmentation using PSO and PCM with Mahalanobis distance,” Expert Systems with Applications, vol. 38, no. 7, pp. 9036–9040, 2011. View at Publisher · View at Google Scholar · View at Scopus
- G. G. Wang, L. Guo, A. H. Gandomi, A. H. Alavi, and H. Duan, “Simulated annealing-based krill herd algorithm for global optimization,” Abstract and Applied Analysis, vol. 2013, Article ID 213853, 11 pages, 2013. View at Publisher · View at Google Scholar
- 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