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Krill herd algorithm based on cuckoo search for solving engineering optimization problems

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Abstract

This paper presents a hybrid krill herd (CSKH) approach to solve structural optimization problems. CSKH improved the Krill herd algorithm (KH) by combining KU/KA operator originated from cuckoo search algorithm (CS) with KH. In CSKH, a greedy selection scheme is used and often overtakes the original KH and CS. In addition, in order to further enhance the assessment of CSKH, a fraction of the worst krill is thrown away and substituted with newly randomly generated ones by KA operator at the end of each generation. The CSKH is applied to five real engineering problems to verify its performance. The experimental results have proven that CSKH algorithm is well capable of solving constrained engineering design problems more efficiently and effectively than the basic CS and KH algorithm.

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References

  1. Abdel-Baset M, Hezam I (2015) An improved flower pollination algorithm based on simulated annealing for solving engineering optimization problems. Asian Journal of Mathematics and Computer Research 3(3):194–170

    Google Scholar 

  2. Abdel-Baset M, Hezam IM (2015) An effective hybrid flower pollination and genetic algorithm for constrained optimization problems. Advanced Engineering Technology and Application An International Journal 4:27–27

    Google Scholar 

  3. Abdel-Baset M, Hezam I (2016) A hybrid flower pollination algorithm for solving ill-conditioned set of equations. International Journal of Bio-Inspired Computation 8(4):215–220

    Google Scholar 

  4. Abdel-Basset M, Hessin A-N, Abdel-Fatah L (2016) A comprehensive study of cuckoo-inspired algorithms. Neural Comput Appl. doi:10.1007/s00521-016-2464-8

  5. Abdel-Raouf O, Metwally MAB (2013) A survey of harmony search algorithm. Int J Comput Appl 70(28):17–26

    Google Scholar 

  6. Abdel-Raouf O, Abdel-Baset M, El-Henawy I (2014) An improved chaotic bat algorithm for solving integer programming problems. International Journal of Modern Education and Computer Science (IJMECS) 6:18

    Google Scholar 

  7. Ahirwal MK, Kumar A, Singh GK (2016) Study of ABC and PSO algorithms as optimised adaptive noise canceller for EEG/ERP. Int J of Bio-Inspired Computation 8(3):170–183

    Google Scholar 

  8. Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014

    Google Scholar 

  9. Akhtar S, Tai K, Ray T (2002) A socio-behavioural simulation model for engineering design optimization. Eng Optim 34(4):341–354

    Google Scholar 

  10. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 I.E. Congress on Evolutionary Computation (CEC 2007), 25–28 Sept. 2007, pp 4661–4667

  11. Bolaji AL, Al-Betar MA, Awadallah MA, Khader AT, Abualigah LM (2016) A comprehensive review: krill herd algorithm (KH) and its applications. Appl Soft Compt 49:437–446

    Google Scholar 

  12. Cagnina LC, Esquivel SC (2008) Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica 32:319–326

    MATH  Google Scholar 

  13. Cai X, Wang L, Kang Q, Wu Q (2014) Bat algorithm with Gaussian walk. Int J of Bio-Inspired Computation 6(3):166–174

    Google Scholar 

  14. Chen B, Shu H, Coatrieux G, Chen G, Sun X, Coatrieux JL (2015) Color image analysis by quaternion-type moments. J Math Imaging Vision 51(1):124–144

    MathSciNet  MATH  Google Scholar 

  15. Cheung NJ, Ding XM, Shen HB (2017) A nonhomogeneous cuckoo search algorithm based on quantum mechanism for real parameter optimization. IEEE Trans Cybern 47(2):391–402

    Google Scholar 

  16. Coelho LS (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37(2):1676–1683

    Google Scholar 

  17. Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127

    Google Scholar 

  18. Coello Coello CA, Mezura Montes E (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16(3):193–203

    Google Scholar 

  19. Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Transactions on Systems, Man, and Cybernetics: Systems 43(4):996–1002

    Google Scholar 

  20. Cui Z, Sun B, Wang G-G, Xue Y, Chen J (2017) A novel oriented cuckoo search algorithm to improve DV-hop performance for cyber-physical systems. J Parallel Distr Com 103:42–52

    Google Scholar 

  21. Deb K (1997) GeneAS: a robust optimal design technique for mechanical component design. In: Dasgupta D, Michalewicz Z (eds) Evolutionary algorithms in engineering applications. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 497–514

    Google Scholar 

  22. Deb K, Goyal M (1996) A combined genetic adaptive search (GeneAS) for engineering design. Computer Science and Informatics 26:30–45

    Google Scholar 

  23. Dimopoulos GG (2007) Mixed-variable engineering optimization based on evolutionary and social metaphors. Comput Method Appl M 196(4–6):803–817

    MATH  Google Scholar 

  24. Djelloul H, Layeb A, Chikhi S (2015) Quantum inspired cuckoo search algorithm for graph colouring problem. Int J of Bio-Inspired Computation 7(3):183–194

    Google Scholar 

  25. Feng Y-H, Wang G-G, Feng Q, Zhao X-J (2014) An effective hybrid cuckoo search algorithm with improved shuffled frog leaping algorithm for 0-1 knapsack problems. Comput Intell Neurosci 2014:1–17

    Google Scholar 

  26. Feng Y, Wang G-G, Gao X-Z (2016) A novel hybrid cuckoo search algorithm with global harmony search for 0-1 knapsack problems. International Journal of Computational Intelligence Systems 9(6):1174–1190

    Google Scholar 

  27. Fu Z, Ren K, Shu J, Sun X, Huang F (2015) Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Transactions on Parallel and Distributed Systems: 27:1–14

  28. Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183

    Google Scholar 

  29. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    MathSciNet  MATH  Google Scholar 

  30. Gandomi AH, Yang X-S, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89(23–24):2325–2336

    Google Scholar 

  31. Gandomi AH, Talatahari S, Tadbiri F, Alavi AH (2013) Krill herd algorithm for optimum design of truss structures. Int J of Bio-Inspired Computation 5(5):281–288

    Google Scholar 

  32. Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput-germany 29(1):17–35

    Google Scholar 

  33. Garg H (2014) Solving structural engineering design optimization problems using an artificial bee colony algorithm. Journal of Industrial and Management Optimization 10(3):777–794

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  35. Ghosh B, Dey B, Bhattacharya A (2015) Solving economic load dispatch problem using hybrid krill herd algorithm. In: 2015 International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE), Shillong, 12–13 June 2015. IEEE, pp 1–6

  36. Goldberg DE (1998) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, New York

    Google Scholar 

  37. Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Transactions on Neural Networks and Learning Systems 26(7):1403–1416

    MathSciNet  Google Scholar 

  38. Guo P, Wang J, Li B, Lee S (2014) A variable threshold-value authentication architecture for wireless mesh networks. Journal of Internet Technology 15(6):929–936

    Google Scholar 

  39. Hafez AI, Hassanien AE, Zawbaa HM, Emary E (2015) Hybrid monkey algorithm with krill herd algorithm optimization for feature selection. In: 2015 11th International Computer Engineering Conference (ICENCO), Cairo, 29–30 Dec. 2015. IEEE, pp 273–277

  40. He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99

    Google Scholar 

  41. He S, Prempain E, Wu QH (2004) An improved particle swarm optimizer for mechanical design optimization problems. Eng Optim 36(5):585–605

    MathSciNet  Google Scholar 

  42. Hedar A-R, Fukushima M (2006) Derivative-free filter simulated annealing method for constrained continuous global optimization. J Glob Optim 35(4):521–549

    MathSciNet  MATH  Google Scholar 

  43. Hezam IM, Abdel-Baset M (2015) An improved flower pollination algorithm for ratios optimization problems. Applied Mathematics & Information Sciences Letters An International Journal 3(2):83–91

    Google Scholar 

  44. Hezam I, Abdel-Baset M (2016) A hybrid flower pollination algorithm for engineering optimization problems. Int J Comput Appl 140(12):10–23

    Google Scholar 

  45. Hu Y, Yin M, Li X (2011) A novel objective function for job-shop scheduling problem with fuzzy processing time and fuzzy due date using differential evolution algorithm. Int J Adv Manuf Technol 56(9):1125–1138

    Google Scholar 

  46. Jia B, Yu B, Wu Q, Yang X, Wei C, Law R, Fu S (2016) Hybrid local diffusion maps and improved cuckoo search algorithm for multiclass dataset analysis. Neurocomputing 189:106–116

    Google Scholar 

  47. Jiang P, Liu F, Wang J, Song Y (2016) Cuckoo search-designated fractal interpolation functions with winner combination for estimating missing values in time series. Appl Math Model 40(23–24):9692–9718. doi:10.1016/j.apm.2016.05.030

    Article  MathSciNet  Google Scholar 

  48. Kannan BK, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Design 116(2):405–411

    Google Scholar 

  49. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    MathSciNet  MATH  Google Scholar 

  50. Kaveh A, Talatahari S (2009) Engineering optimization with hybrid particle swarm and ant colony optimization. Asian J Civil Eng 10(6):611–628

    Google Scholar 

  51. Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27(1):155–182

    MATH  Google Scholar 

  52. Kavousi-Fard A, Akbari-Zadeh M-R, Dehghan B, Kavousi-Fard F (2014) A novel sufficient bio-inspired optimisation method based on modified krill herd algorithm to solve the economic load dispatch. Int J of Bio-Inspired Computation 6(6):416–423

    Google Scholar 

  53. Kennedy J, Eberhart R (1995) Particle swarm optimization. Paper presented at the Proceeding of the IEEE International Conference on Neural Networks, Perth, 27 November-1 December

  54. Khatib W, Fleming P (1998) The stud GA: a mini revolution? In: Eiben A, Bäck T, Schoenauer M, Schwefel H-P (eds) Parallel problem solving from nature - PPSN V, vol 1498. Lecture notes in computer science. Springer Berlin Heidelberg, London, pp 683–691

    Google Scholar 

  55. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    MathSciNet  MATH  Google Scholar 

  56. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Method Appl M 194(36–38):3902–3933

    MATH  Google Scholar 

  57. Li HL, Papalambros P (1985) A production system for use of global optimization knowledge. J Mech Transm Autom Des 107(2):277–284

    Google Scholar 

  58. Li X, Yin M (2013) Multiobjective binary biogeography based optimization for feature selection using gene expression data. IEEE Trans Nanobioscience 12(4):343–353

    Google Scholar 

  59. Li X, Yin M (2015) Modified cuckoo search algorithm with self adaptive parameter method. Inf Sci 298:80–97

    Google Scholar 

  60. Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518

    Google Scholar 

  61. Li Z, Zhou Y, Zhang S, Song J (2016) Lévy-flight moth-flame algorithm for function optimization and engineering design problems. Math Probl Eng 2016:1–22

    Google Scholar 

  62. Liu Y, Zhang X, Cui J, Wu C, Aghajan H, Zha H (2010) Visual analysis of child-adult interactive behaviors in video sequences. In: 2010 16th International Conference on Virtual Systems and Multimedia, 20–23 Oct. 2010, pp 26–33

  63. Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Compt 10(2):629–640

    Google Scholar 

  64. Liu Y, Cui J, Zhao H, Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 11–15 Nov. 2012, pp 898–901

  65. Liu Y, Liang Y, Liu S, Rosenblum DS, Zheng Y (2016) Predicting urban water quality with ubiquitous data. arXiv preprint arXiv:161009462

  66. Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016) Urban water quality prediction based on multi-task multi-view learning. In: 25th International Joint Conference on Artificial Intelligence IJCAI-16, New York, p 9-15 July 2016. AAAI

  67. Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: predicting your career path. In: Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, February 12–17 2016, pp 201–207

  68. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Google Scholar 

  69. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2016) Action2activity: recognizing complex activities from sensor data. arXiv preprint arXiv:161101872:

  70. Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing complex activities by a probabilistic interval-based model. In: Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, February 12–17 2016. p 201–207

  71. Long W, Zhang W-z, Huang Y-f, Chen Y-x (2014) A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization. J Cent South Univ 21(8):3197–3204

    Google Scholar 

  72. Lu Y, Wei Y, Liu L, Zhong J, Sun L, Liu Y (2016) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimedia Tools and Applications. doi:10.1007/s11042-015-3188-y

  73. Mehta VK, Dasgupta B (2012) A constrained optimization algorithm based on the simplex search method. Eng Optim 44(5):537–550

    MathSciNet  Google Scholar 

  74. Mezura-Montes E, Coello CAC (2005) Useful infeasible solutions in engineering optimization with evolutionary algorithms. In: Gelbukh A, de Albornoz Á, Terashima-Marín H (eds) MICAI 2005: advances in artificial intelligence: 4th Mexican international conference on artificial intelligence, Monterrey, Mexico, November 14–18, 2005. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 652–662

  75. Mezura-Montes E, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37(4):443–473

    MathSciNet  MATH  Google Scholar 

  76. Mezura-Montes E, Coello CAC, Landa-Becerra R (2003) Engineering optimization using simple evolutionary algorithm. In: 15th IEEE International Conference on Tools with Artificial Intelligence, 3–5 Nov. 2003. IEEE, pp 149–156

  77. Mezura-Montes E, Coello Coello CA, Velázquez-Reyes J, Muñoz-Dávila L (2007) Multiple trial vectors in differential evolution for engineering design. Eng Optim 39(5):567–589

    MathSciNet  Google Scholar 

  78. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Google Scholar 

  79. Mirjalili S, Lewis A (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput & Applic 25(7–8):1569–1584

    Google Scholar 

  80. Mirjalili S, Mirjalili SM, Lewis A (2014) Let a biogeography-based optimizer train your multi-layer perceptron. Inf Sci 269:188–209

    MathSciNet  Google Scholar 

  81. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  82. Mukherjee A, Mukherjee V (2015) Solution of optimal power flow using chaotic krill herd algorithm. Chaos, Solitons Fractals 78:10–21

    MathSciNet  Google Scholar 

  83. Pan W-T (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl-Based Syst 26:69–74

    Google Scholar 

  84. Pan Z, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast 61(2):166–176

    Google Scholar 

  85. Raouf O, El-henawy I, Abdel-Baset M (2014) A novel hybrid flower pollination algorithm with chaotic harmony search for solving Sudoku puzzles. International Journal of Modern Education and Computer Science 3:38–44

    Google Scholar 

  86. Raouf OA, Baset MA, Elhenawy IM (2014) Improved harmony search algorithm with chaos for solving definite integral. International Journal of Operational Research 21(2):252–261

    MathSciNet  Google Scholar 

  87. Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evolut Comput 7(4):386–396

    Google Scholar 

  88. Ray T, Saini P (2001) Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Optim 33(6):735–748

    Google Scholar 

  89. Ren Y, Shen J, Wang J, Han J, Lee S (2015) Mutual verifiable provable data auditing in public cloud storage. Journal of Internet Technology 16(2):317–323

    Google Scholar 

  90. Rezoug A, Boughaci D (2016) A self-adaptive harmony search combined with a stochastic local search for the 0-1 multidimensional knapsack problem. Int J of Bio-Inspired Computation 8(4):234–239

    Google Scholar 

  91. Rostami M, Kavousi-Fard A, Niknam T (2015) Expected cost minimization of smart grids with plug-in hybrid electric vehicles using optimal distribution feeder reconfiguration. IEEE Trans Ind Inf 11(2):388–397

    Google Scholar 

  92. Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Compt 13(5):2592–2612

    Google Scholar 

  93. Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Design 112(2):223–229

    Google Scholar 

  94. Sekhar P, Mohanty S (2016) An enhanced cuckoo search algorithm based contingency constrained economic load dispatch for security enhancement. Int J Elec Power 75:303–310

    Google Scholar 

  95. Shah-Hosseini H (2009) The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int J of Bio-Inspired Computation 1(1):71–79

    Google Scholar 

  96. Shen J, Tan H, Wang J, Wang J, Lee S (2015) A novel routing protocol providing good transmission reliability in underwater sensor networks. Journal of Internet Technology 16(1):171–178

    Google Scholar 

  97. Shi Y (2011) An optimization algorithm based on brainstorming process. International Journal of Swarm Intelligence Research 2(4):35–62

    Google Scholar 

  98. Shi Y, Xue J, Wu Y (2013) Multi-objective optimization based on brain storm optimization algorithm. International Journal of Swarm Intelligence Research 4(3):1–21

    Google Scholar 

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

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  101. Sun S, Qi H, Zhao F, Ruan L, Li B (2016) Inverse geometry design of two-dimensional complex radiative enclosures using krill herd optimization algorithm. Appl Therm Eng 98:1104–1115. doi:10.1016/j.applthermaleng.2016.01.017

    Article  Google Scholar 

  102. Tan Y (2015) Fireworks algorithm-a novel swarm intelligence optimization method. Springer-Verlag Berlin Heidelberg, Berlin

    MATH  Google Scholar 

  103. Tsai J-F (2005) Global optimization of nonlinear fractional programming problems in engineering design. Eng Optim 37(4):399–409

    MathSciNet  Google Scholar 

  104. Wang G-G (2016) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing. doi:10.1007/s12293-016-0212-3

  105. Wang G-G, Guo L, Duan H, Liu L, Wang H (2012) The model and algorithm for the target threat assessment based on Elman_AdaBoost strong predictor. Acta Electron Sin 40(5):901–906

    Google Scholar 

  106. Wang G, Guo L, Duan H, Liu L, Wang H, Wang J (2012) A hybrid meta-heuristic DE/CS algorithm for UCAV path planning. J Inform Comput Sci 9(16):4811–4818

    Google Scholar 

  107. 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

    Google Scholar 

  108. Wang G-G, Gandomi AH, Alavi AH (2013) A chaotic particle-swarm krill herd algorithm for global numerical optimization. Kybernetes 42(6):962–978

    MathSciNet  Google Scholar 

  109. Wang G-G, Gandomi AH, Yang X-S, Alavi AH (2014) A novel improved accelerated particle swarm optimization algorithm for global numerical optimization. Eng Comput 31(7):1198–1220

    Google Scholar 

  110. Wang G-G, Gandomi AH, Alavi AH, Hao G-S (2014) Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput & Applic 25(2):297–308

    Google Scholar 

  111. Wang G-G, Guo L, Gandomi AH, Hao G-S, Wang H (2014) Chaotic krill herd algorithm. Inf Sci 274:17–34

    MathSciNet  Google Scholar 

  112. Wang G, Guo L, Wang H, Duan H, Liu L, Li J (2014) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput & Applic 24(3–4):853–871

    Google Scholar 

  113. Wang G-G, Gandomi AH, Alavi AH (2014) Stud krill herd algorithm. Neurocomputing 128:363–370

    Google Scholar 

  114. Wang G-G, Gandomi AH, Alavi AH (2014) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 38(9–10):2454–2462. doi:10.1016/j.apm.2013.10.052

    Article  MathSciNet  MATH  Google Scholar 

  115. Wang G-G, Deb S, Coelho LdS (2015) Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Int J of Bio-Inspired Computation. doi:10.1504/IJBIC.2015.10004283

  116. Wang G-G, Deb S, Coelho LdS (2015) Elephant herding optimization. In: 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI 2015), Bali, Indonesia, December 7–9 2015. IEEE, pp 1–5.

  117. Wang G-G, Deb S, Gandomi AH, Alavi AH (2015) A hybrid PBIL-based krill herd algorithm. In: 2015 3rd International Symposium on Computational and Business Intelligence, Bali, Indonesia, December 7–8, 2015. IEEE, pp 39–44

  118. Wang G-G, Deb S, Gao X-Z, Coelho LS (2016) A new metaheuristic optimization algorithm motivated by elephant herding behavior. Int J of Bio-Inspired Computation 8(6):394–409

    Google Scholar 

  119. Wang G-G, Gandomi AH, Alavi AH, Dong Y-Q (2016) A hybrid meta-heuristic method based on firefly algorithm and krill herd. In: Samui P (ed) Handbook of research on advanced computational techniques for simulation-based engineering. IGI, Hershey, pp 521–540

    Google Scholar 

  120. Wang G-G, Gandomi AH, Alavi AH, Deb S (2016) A hybrid method based on krill herd and quantum-behaved particle swarm optimization. Neural Comput & Applic 27(4):989–1006

    Google Scholar 

  121. Wang G-G, Gandomi AH, Yang X-S, Alavi AH (2016) A new hybrid method based on krill herd and cuckoo search for global optimization tasks. Int J of Bio-Inspired Computation 8(5):286–299

    Google Scholar 

  122. Wang G-G, Deb S, Gandomi AH, Alavi AH (2016) Opposition-based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing 177:147–157

    Google Scholar 

  123. Wang G-G, Gandomi AH, Alavi AH, Deb S (2016) A multi-stage krill herd algorithm for global numerical optimization. Int J Artif Intell Tools 25(2):1550030

    Google Scholar 

  124. Wang G-G, Deb S, Gandomi AH, Zhang Z, Alavi AH (2016) Chaotic cuckoo search. Soft Comput 20(9):3349–3362

    Google Scholar 

  125. Wang G-G, Gandomi AH, Zhao X, Chu HE (2016) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20(1):273–285

    Google Scholar 

  126. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406

    Google Scholar 

  127. Xie S, Wang Y (2014) Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wirel Pers Commun 78(1):231–246

    Google Scholar 

  128. Yang X-S (2010) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, Frome

    Google Scholar 

  129. Yi J-H, Wang J, Wang G-G (2016) Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem. Adv Mech Eng 8(1):1–13

    Google Scholar 

  130. Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178(15):3043–3074

    Google Scholar 

  131. Zhou Y, Zhou G, Zhang J (2013) A hybrid glowworm swarm optimization algorithm for constrained engineering design problems. Applied Mathematics & Information Sciences 7(1):379–388

    Google Scholar 

  132. Zou D, Gao L, Li S, Wu J (2011) Solving 0-1 knapsack problem by a novel global harmony search algorithm. Appl Soft Compt 11(2):1556–1564

    Google Scholar 

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Acknowledgements

This work was supported by the Natural Science Foundation of Jiangsu Province (No. BK20150239), National Natural Science Foundation of China (No. 61503165, No. 61673196, and No. 61402207), and The Open Research Fund of Sichuan Key Laboratory for Nature Gas and Geology (No.2015trqdz04).

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  1. Faculty of Computers and Informatics, Department of Operations Research, Zagazig University, Zagazig, Egypt

    Mohamed Abdel-Basset

  2. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu, 221116, China

    Gai-Ge Wang

  3. School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, 632014, India

    Arun Kumar Sangaiah

  4. Faculty of Computers and Informatics, Department of Information Technology, Zagazig University, Zagazig, Egypt

    Ehab Rushdy

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Correspondence to Mohamed Abdel-Basset.

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Abdel-Basset, M., Wang, GG., Sangaiah, A.K. et al. Krill herd algorithm based on cuckoo search for solving engineering optimization problems. Multimed Tools Appl 78, 3861–3884 (2019). https://doi.org/10.1007/s11042-017-4803-x

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  • DOI: https://doi.org/10.1007/s11042-017-4803-x

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