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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 682073, 14 pages
Lévy-Flight Krill Herd Algorithm
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin 130033, China
2University of Chinese Academy of Sciences, Beijing 100039, China
3Department of Civil Engineering, University of Akron, Akron, OH 44325
4Department of Civil and Environmental Engineering, Engineering Building, Michigan State University, East Lansing, MI 48824, USA
5School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
Received 3 November 2012; Accepted 20 December 2012
Academic Editor: Siamak Talatahari
Copyright © 2013 Gaige Wang 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.
- S. Gholizadeh and F. Fattahi, “Design optimization of tall steel buildings by a modified particle swarm algorithm,” The Structural Design of Tall and Special Buildings. In press.
- S. Talatahari, R. Sheikholeslami, M. Shadfaran, and M. Pourbaba, “Optimum design of gravity retaining walls using charged system search algorithm,” Mathematical Problems in Engineering, vol. 2012, Article ID 301628, 10 pages, 2012.
- X. S. Yang, A. H. Gandomi, S. Talatahari, and A. H. Alavi, Metaheuristics in Water, Geotechnical and Transport Engineering, 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.
- S. Gholizadeh and A. Barzegar, “Shape optimization of structures for frequency constraints by sequential harmony search algorithm,” Engineering Optimization. In press.
- S. Chen, Y. Zheng, C. Cattani, and W. Wang, “Modeling of biological intelligence for SCM system optimization,” Computational and Mathematical Methods in Medicine, vol. 2010, Article ID 769702, 10 pages, 2012.
- X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, Frome, UK, 2nd edition, 2010.
- X. S. Yang, Engineering Optimization: An Introduction with Metaheuristic Applications, Wiley & Sons, NJ, USA, 2010.
- G. Wang, L. Guo, H. Duan, L. Liu, H. Wang, and M. Shao, “Path planning for uninhabited combat aerial vehicle using hybrid meta-heuristic DE/BBO algorithm,” Advanced Science, Engineering and Medicine, vol. 4, no. 6, pp. 550–564, 2012.
- G. Wang, L. Guo, H. Duan, L. Liu, and H. Wang, “A bat algorithm with mutation for UCAV path planning,” The Scientific World Journal, vol. 2012, Article ID 418946, 15 pages, 2012.
- H. Duan, W. Zhao, G. Wang, and X. Feng, “Test-sheet composition using analytic hierarchy process and hybrid metaheuristic algorithm TS/BBO,” Mathematical Problems in Engineering, vol. 2012, Article ID 712752, 22 pages, 2012.
- W.-H. Ho and A. L.-F. Chan, “Hybrid Taguchi-differential evolution algorithm for parameter estimation of differential equation models with application to HIV dynamics,” Mathematical Problems in Engineering, vol. 2011, Article ID 514756, 14 pages, 2011.
- D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York, NY, USA, 1998.
- M. Shahsavar, A. A. Najafi, and S. T. A. Niaki, “Statistical design of genetic algorithms for combinatorial optimization problems,” Mathematical Problems in Engineering, vol. 2011, Article ID 872415, 17 pages, 2011.
- G. Wang and L. Guo, “A novel hybrid bat algorithm with harmony search for global numerical optimization,” Journal of Applied Mathematics. In press.
- 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.
- 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.
- A. H. Gandomi, X.-S. Yang, S. Talatahari, and S. Deb, “Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization,” Computers & Mathematics with Applications, vol. 63, no. 1, pp. 191–200, 2012.
- A. H. Gandomi and A. H. Alavi, “Multi-stage genetic programming: a new strategy to nonlinear system modeling,” Information Sciences, vol. 181, no. 23, pp. 5227–5239, 2011.
- 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.
- G. Wang and L. Guo, “Hybridizing harmony search with biogeography based optimization for global numerical optimization,” Journal of Computational and Theoretical Nanoscience. In press.
- J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, December 1995.
- S. Talatahari, M. Kheirollahi, C. Farahmandpour, and A. H. Gandomi, “A multi-stage particle swarm for optimum design of truss structures,” Neural Computing & Applications. In press.
- A. H. Gandomi, G. J. Yun, X. -S. Yang, and S. Talatahari, “Chaos-enhanced accelerated particle swarm optimization,” Communications in Nonlinear Science and Numerical Simulation, vol. 18, no. 2, pp. 327–340, 2013.
- 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. 1–19, 2013.
- G. Wang, L. Guo, H. Duan, L. Liu, H. Wang, and W. Jianbo, “A hybrid meta-heuristic DE/CS algorithm for UCAV path planning,” Journal of Information and Computational Science, vol. 9, no. 16, pp. 1–8, 2012.
- 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.
- G. Wang, L. Guo, H. Duan, H. Wang, L. Liu, and J. Li, “Incorporating mutation scheme into krill herd algorithm for global numerical optimization,” Neural Computing and Applications. In press.
- G. Wang, L. Guo, H. Duan, H. Wang, and L. Liu, “A new improved firefly algorithm for global numerical optimization,” Journal of Computational and Theoretical Nanoscience. In press.
- G. Wang, L. Guo, H. Duan, H. Wang, L. Liu, and M. Shao, “A hybrid meta-heuristic DE/CS algorithm for UCAV three-dimension path planning,” The Scientific World Journal, vol. 2012, Article ID 583973, 11 pages, 2012.
- G. Wang, L. Guo, A. H. Gandomi et al., “A new improved krill herd algorithm for global numerical optimization,” Neurocomputing. In press.
- S. Yang and J. Lee, “Multi-basin particle swarm intelligence method for optimal calibration of parametric Lévy models,” Expert Systems with Applications, vol. 39, no. 1, pp. 482–493, 2012.
- P. Barthelemy, J. Bertolotti, and D. S. Wiersma, “A Lévy flight for light,” Nature, vol. 453, no. 7194, pp. 495–498, 2008.
- A. Natarajan, S. Subramanian, and K. Premalatha, “A comparative study of cuckoo search and bat algorithm for Bloom filter optimisation in spam filtering,” International Journal of Bio-Inspired Computation, vol. 4, no. 2, pp. 89–99, 2012.
- X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 82–102, 1999.
- D. Simon, “Biogeography-based optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702–713, 2008.
- X. Li, J. Wang, J. Zhou, and M. Yin, “A perturb biogeography based optimization with mutation for global numerical optimization,” Applied Mathematics and Computation, vol. 218, no. 2, pp. 598–609, 2011.
- D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, 2007.
- M. Dorigo and T. Stutzle, Ant Colony Optimization, MIT Press, Cambridge, Mass, USA, 2004.
- X. S. Yang and S. Deb, “Engineering optimisation by cuckoo search,” International Journal of Mathematical Modelling and Numerical Optimisation, vol. 1, no. 4, pp. 330–343, 2010.
- H.-G. Beyer, The Theory of Evolution Strategies, Springer, Berlin, Germany, 2001.
- B. Shumeet, “Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning,” Tech. Rep. CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, Pa, USA, 1994.
- G. Wang, L. Guo, H. Duan, L. Liu, and H. Wang, “Dynamic deployment of wireless sensor networks by biogeography based optimization algorithm,” Journal of Sensor and Actuator Networks, vol. 1, no. 2, pp. 86–96, 2012.
- K. Tang, X. Li, P. N. Suganthan, Z. Yang, and T. Weise, “Benchmark functions for the CEC'2010 special session and competition on large scale global optimization,” Inspired Computation and Applications Laboratory, USTC, Hefei, China, 2010.
- R. Mallipeddi and P. Suganthan, “Problem definitions and evaluation criteria for the CEC 2010 Competition on Constrained Real-Parameter Optimization,” Nanyang Technological University, Singapore, 2010.
- P. Lu, S. Chen, and Y. Zheng, “Artificial intelligence in civil engineering,” Mathematical Problems in Engineering, vol. 2012, Article ID 145974, 22 pages, 2012.