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Incorporating mutation scheme into krill herd algorithm for global numerical optimization

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An Erratum to this article was published on 03 May 2013

Abstract

Recently, Gandomi and Alavi proposed a robust meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization. To improve the performance of the KH algorithm, harmony search (HS) is applied to mutate between krill during the process of krill updating instead of physical diffusion used in KH. A novel hybrid meta-heuristic optimization approach HS/KH is proposed to solve global numerical optimization problem. HS/KH combines the exploration of harmony search (HS) with the exploitation of KH effectively, and hence, it can generate the promising candidate solutions. The detailed implementation procedure for this improved meta-heuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most cases, the performance of this hybrid meta-heuristic method (HS/KH) is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, HS, KH, PSO, and SGA. The effect of the HS/FA parameters is also analyzed.

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References

  1. Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Metaheuristic applications in structures and infrastructures. Elsevier, London, UK

    Google Scholar 

  2. Yang XS, Gandomi AH, Talatahari S, Alavi AH (2013) Metaheuristics in water. Geotechnical and Transport Engineering, Elsevier

    Google Scholar 

  3. Goldberg DE (1998) Genetic algorithms in search. Optimization and Machine learning, Addison-Wesley

    Google Scholar 

  4. Zhao M, Ren J, Ji L, Fu C, Li J, Zhou M (2012) Parameter selection of support vector machines and genetic algorithm based on change area search. Neural Comput Appl 21(1):1–8. doi:10.1007/s00521-011-0603-9

    Article  MATH  Google Scholar 

  5. Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Article  MATH  MathSciNet  Google Scholar 

  6. Gandomi AH, Yang X-S, Talatahari S, Deb S (2012) Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Comput Math Appl 63(1):191–200. doi:10.1016/j.camwa.2011.11.010

    Article  MATH  MathSciNet  Google Scholar 

  7. Khazraee S, Jahanmiri A, Ghorayshi S (2011) Model reduction and optimization of reactive batch distillation based on the adaptive neuro-fuzzy inference system and differential evolution. Neural Comput Appl 20(2):239–248. doi:10.1007/s00521-010-0364-x

    Article  Google Scholar 

  8. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948

  9. Chen D, Zhao C, Zhang H (2011) An improved cooperative particle swarm optimization and its application. Neural Comput Appl 20(2):171–182. doi:10.1007/s00521-010-0503-4

    Article  Google Scholar 

  10. Talatahari S, Kheirollahi M, Farahmandpour C, Gandomi AH (2012) A multi-stage particle swarm for optimum design of truss structures. Neural Comput Appl. doi:10.1007/s00521-012-1072-5

    Google Scholar 

  11. Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part II: Geotechnical and Earthquake Engineering Problems. Neural Comput Appl 21 (1):189–201

    Google Scholar 

  12. Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci 181(23):5227–5239. doi:10.1016/j.ins.2011.07.026

    Article  Google Scholar 

  13. Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12(6):702–713

    Article  Google Scholar 

  14. Wang G, Guo L, Duan H, Liu L, Wang H (2012) Dynamic deployment of wireless sensor networks by biogeography based optimization algorithm. J Sens Actuat Netw 1(2):86–96. doi:10.3390/jsan1020086

    Article  Google Scholar 

  15. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

  16. Gandomi AH, Yang X-S, Alavi AH, Talatahari S (2012) Bat algorithm for constrained optimization tasks. Neural Comput Appl. doi:10.1007/s00521-012-1028-9

    Google Scholar 

  17. Gandomi AH, Yang X-S, Alavi AH (2012) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput Ger. doi:10.1007/s00366-011-0241-y

    Google Scholar 

  18. Gandomi AH, Talatahari S, Yang XS, Deb S (2012) Design optimization of truss structures using cuckoo search algorithm. Struct Des Tall Spec. doi:10.1002/tal.1033

    Google Scholar 

  19. Wang G, Guo L, Duan H, Wang H, Liu L, Shao M (2012) A hybrid meta-heuristic DE/CS algorithm for UCAV three-dimension path planning. Sci World J 2012:1–11. doi:10.1100/2012/583973

  20. Yang X-S, Sadat Hosseini SS, Gandomi AH (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 12(3):1180–1186. doi:10.1016/j.asoc.2011.09.017

    Article  Google Scholar 

  21. Gandomi AH, Yang X-S, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89(23–24):2325–2336. doi:10.1016/j.compstruc.2011.08.002

    Article  Google Scholar 

  22. Talatahari S, Gandomi AH, Yun GJ (2012) Optimum design of tower structures using Firefly Algorithm. Struct Des Tall Spec

  23. Wang G, Guo L, Duan H, Liu L, Wang H (2012) A modified firefly algorithm for UCAV path planning. Int J Hybrid Inf Technol 5(3):123–144

    Google Scholar 

  24. Gandomi AH, Alavi AH (2012) Krill Herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simulat 17(12):4831–4845. doi:10.1016/j.cnsns.2012.05.010

    Article  MATH  MathSciNet  Google Scholar 

  25. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. doi:10.1177/003754970107600201

    Article  Google Scholar 

  26. Wang G, Guo L, Duan H, Wang H, Liu L, Shao M (2012) Hybridizing Harmony Search with Biogeography based Optimization for Global Numerical Optimization. J Comput Theor Nanosci

  27. Yang X-S (2011) Optimization Algorithms. In: Koziel S, Yang X-S (eds) Computational Optimization, Methods and Algorithms, vol 356. Studies in Computational Intelligence. Springer-Verlag Berlin Heidelberg, Berlin, Heidelberg, pp. 13–31. doi: 10.1007/978-3-642-20859-1_2

  28. Zhao SZ, Suganthan PN, Pan Q-K, Fatih Tasgetiren M (2011) Dynamic multi-swarm particle swarm optimizer with harmony search. Expert Syst Appl 38(4):3735–3742. doi:10.1016/j.eswa.2010.09.032

    Article  Google Scholar 

  29. Dorigo M, Stutzle T (2004) Ant Colony Optimization. MIT Press, Cambridge

    Book  MATH  Google Scholar 

  30. Duan H, Zhao W, Wang G, Feng X (2012) Test-sheet composition using AHP and TS/BBO. Math Probl Eng 2012:1–22. doi:10.1155/2012/712752

  31. Wang G, Guo L, Duan H, Liu L, Wang H, Shao M (2012) Path planning for uninhabited combat aerial vehicle using Hybrid Meta-Heuristic DE/BBO algorithm. Adv Sci Eng Med 4(6):550–564. doi:10.1166/asem.2012.1223

    Article  Google Scholar 

  32. Beyer H (2001) The theory of evolution strategies. Springer, New York

    Book  Google Scholar 

  33. Gandomi AH, Yun GJ, Yang X-S, Talatahari S (2013) Chaos-enhanced accelerated particle swarm optimization. Commun Nonlinear Sci Numer Simulat 18(2):327–340. doi:10.1016/j.cnsns.2012.07.017

    Article  MATH  MathSciNet  Google Scholar 

  34. Khatib W, Fleming P (1998) The stud GA: A mini revolution? In: Eiben A, Back T, Schoenauer M, Schwefel H (eds) Proceeding of the 5th International Conference on Parallel Problem Solving from Nature (1998) Parallel problem solving from nature. Springer-Verlag, London, pp 683–691

    Google Scholar 

  35. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3(2):82–102

    Article  Google Scholar 

  36. Fletcher R, Powell MJD (1963) A rapidly convergent descent method for minimization. Comput J 6(2):163–168

    Article  MATH  MathSciNet  Google Scholar 

  37. Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86–92. doi:citeulike-article-id:7471117

    Article  Google Scholar 

  38. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10(3):281–295. doi:10.1109/tevc.2005.857610

    Article  Google Scholar 

  39. Omran MGH, Mahdavi M (2008) Global-best harmony search. Appl Math Comput 198(2):643–656. doi:10.1016/j.amc.2007.09.004

    Article  MATH  MathSciNet  Google Scholar 

  40. Brits R, Engelbrecht A, Van den Bergh F (2007) Locating multiple optima using particle swarm optimization. Appl Math Comput 189(2):1859–1883

    Article  MATH  MathSciNet  Google Scholar 

  41. Tang K, Li X, Suganthan PN, Yang Z, Weise T (2010) Benchmark functions for the CEC’2010 special session and competition on large scale global optimization. Nature Inspired Computation and Applications Laboratory, USTC

    Google Scholar 

  42. Mallipeddi R, Suganthan P (2010) Problem definitions and evaluation criteria for the CEC 2010 competition on constrained real-parameter optimization. Nanyang Technological University, Singapore

    Google Scholar 

Download references

Acknowledgments

This work was supported by State Key Laboratory of Laser Interaction with Material Research Fund under Grant No. SKLLIM0902-01 and Key Research Technology of Electric-discharge Non-chain Pulsed DF Laser under Grant No. LXJJ-11-Q80.

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Authors and Affiliations

  1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China

    Gaige Wang, Lihong Guo, Heqi Wang, Luo Liu & Jiang Li

  2. Graduate School of Chinese Academy of Sciences, Beijing, 100039, China

    Gaige Wang, Luo Liu & Jiang Li

  3. School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China

    Hong Duan

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  1. Gaige Wang
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  2. Lihong Guo
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  3. Heqi Wang
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  4. Hong Duan
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  5. Luo Liu
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  6. Jiang Li
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Correspondence to Lihong Guo.

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Wang, G., Guo, L., Wang, H. et al. Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput & Applic 24, 853–871 (2014). https://doi.org/10.1007/s00521-012-1304-8

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  • DOI: https://doi.org/10.1007/s00521-012-1304-8

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