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Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Networks

  • Ali Asghar Heidari
  • Hossam Faris
  • Seyedali MirjaliliEmail author
  • Ibrahim Aljarah
  • Majdi Mafarja
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 811)

Abstract

This chapter proposes an efficient hybrid training technique (ALOMLP) based on the Ant Lion Optimizer (ALO) to be utilized in dealing with Multi-Layer Perceptrons (MLPs) neural networks. ALO is a well-regarded swarm-based meta-heuristic inspired by the intelligent hunting tricks of antlions in nature. In this chapter, the theoretical backgrounds of ALO are explained in details first. Then, a comprehensive literature review is provided based on recent well-established works from 2015 to 2018. In addition, a convenient encoding scheme is presented and the objective formula is defined, mathematically. The proposed training model based on ALO algorithm is substantiated on sixteen standard datasets. The efficiency of ALO is compared with differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and population-based incremental learning (PBIL) in terms of best, worst, average, and median accuracies. Furthermore, the convergence propensities are monitored and analyzed for all competitors. The experiments show that the ALOMLP outperforms GA, PBIL, DE, and PSO in classifying the majority of datasets and provides improved accuracy results and convergence rates.

References

  1. 1.
    Abbassi, R., Abbassi, A., Heidari, A. A., & Mirjalili, S. (2019). An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Conversion and Management, 179, 362–372.Google Scholar
  2. 2.
    Ahmad, S., Mafarja, M., Faris, H., & Aljarah, I. (2018). Feature selection using salp swarm algorithm with chaos.Google Scholar
  3. 3.
    Alba, E., & Chicano, J. (2004). Training neural networks with ga hybrid algorithms. In Genetic and Evolutionary Computation–GECCO 2004 (pp. 852–863). Springer.Google Scholar
  4. 4.
    Ali, E., Elazim, S. A., & Abdelaziz, A. (2017). Ant lion optimization algorithm for optimal location and sizing of renewable distributed generations. Renewable Energy, 101, 1311–1324.Google Scholar
  5. 5.
    Ali, E., Elazim, S. A., & Abdelaziz, A. (2018). Optimal allocation and sizing of renewable distributed generation using ant lion optimization algorithm. Electrical Engineering, 100(1), 99–109.Google Scholar
  6. 6.
    Aljarah, I., AlaM, A. Z., Faris, H., Hassonah, M. A., Mirjalili, S., & Saadeh, H. (2018). Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognitive Computation (pp. 1–18).Google Scholar
  7. 7.
    Aljarah, I., Faris, H., & Mirjalili, S. (2016). Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Computing (pp. 1–15).Google Scholar
  8. 8.
    Aljarah, I., Faris, H., Mirjalili, S., & Al-Madi, N. (2018). Training radial basis function networks using biogeography-based optimizer. Neural Computing and Applications, 29(7), 529–553.Google Scholar
  9. 9.
    Aljarah, I., & Ludwig, S. A. (2012). Parallel particle swarm optimization clustering algorithm based on mapreduce methodology. In Proceedings of the Fourth World Congress on Nature and Biologically Inspired Computing (IEEE NaBIC12). IEEE Explore.Google Scholar
  10. 10.
    Aljarah, I., & Ludwig, S. A. (2013). A new clustering approach based on glowworm swarm optimization. In Proceedings of 2013 IEEE Congress on Evolutionary Computation Conference (IEEE CEC13), Cancun, Mexico. IEEE Xplore.Google Scholar
  11. 11.
    Aljarah, I., Mafarja, M., Heidari, A. A., Faris, H., Zhang, Y., & Mirjalili, S. (2018). Asynchronous accelerating multi-leader salp chains for feature selection. Applied Soft Computing, 71, 964–979.Google Scholar
  12. 12.
    Almonacid, F., Fernandez, E. F., Mellit, A., & Kalogirou, S. (2017). Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology. Renewable and Sustainable Energy Reviews, 75, 938–953.Google Scholar
  13. 13.
    Ata, R. (2015). Artificial neural networks applications in wind energy systems: a review. Renewable and Sustainable Energy Reviews, 49, 534–562.Google Scholar
  14. 14.
    Aminisharifabad, M., Yang, Q. & Wu, X. (2018). A penalized Autologistic regression with application for modeling the microstructure of dual-phase high strength steel. Journal of Quality Technology. in-press.Google Scholar
  15. 15.
    Blum, C., & Socha, K. (2005). Training feed-forward neural networks with ant colony optimization: An application to pattern classification. In Fifth International Conference on Hybrid Intelligent Systems, 2005. HIS’05 (p. 6). IEEE.Google Scholar
  16. 16.
    Braik, M., Sheta, A., & Arieqat, A.: (2008). A comparison between gas and pso in training ann to model the te chemical process reactor. In AISB 2008 Convention Communication, Interaction And Social Intelligence (vol. 1, p. 24).Google Scholar
  17. 17.
    Cao, W., Yan, C., Wu, D., & Tuo, J. (2017). A novel multi-objective optimization approach of machining parameters with small sample problem in gear hobbing. The International Journal of Advanced Manufacturing Technology, 93(9–12), 4099–4110.Google Scholar
  18. 18.
    Chaudhuri, B., & Bhattacharya, U. (2000). Efficient training and improved performance of multilayer perceptron in pattern classification. Neurocomputing, 34(1), 11–27.zbMATHGoogle Scholar
  19. 19.
    Chen, J. F., Do, Q. H., & Hsieh, H. N. (2015). Training artificial neural networks by a hybrid pso-cs algorithm. Algorithms, 8(2), 292–308.MathSciNetGoogle Scholar
  20. 20.
    Chitsaz, H., & Aminisharifabad, M. (2015). Exact learning of rna energy parameters from structure. Journal of Computational Biology, 22(6), 463–473.MathSciNetGoogle Scholar
  21. 21.
    Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems (MCSS), 2(4), 303–314.MathSciNetzbMATHGoogle Scholar
  22. 22.
    Ding, S., Li, H., Su, C., Yu, J., & Jin, F. (2013). Evolutionary artificial neural networks: A review. Artificial Intelligence Review (pp. 1–10).Google Scholar
  23. 23.
    Dinkar, S. K., & Deep, K. (2017). Opposition based laplacian ant lion optimizer. Journal of Computational Science, 23, 71–90.MathSciNetGoogle Scholar
  24. 24.
    Dubey, H. M., Pandit, M., & Panigrahi, B. (2016). Ant lion optimization for short-term wind integrated hydrothermal power generation scheduling. International Journal of Electrical Power & Energy Systems, 83, 158–174.Google Scholar
  25. 25.
    Dubey, H. M., Pandit, M., & Panigrahi, B. (2018). An overview and comparative analysis of recent bio-inspired optimization techniques for wind integrated multi-objective power dispatch. Swarm and Evolutionary Computation, 38, 12–34.Google Scholar
  26. 26.
    Elaziz, M. A., Moemen, Y. S., Hassanien, A. E., & Xiong, S. (2018). Quantitative structure-activity relationship model for hcvns5b inhibitors based on an antlion optimizer-adaptive neuro-fuzzy inference system. Scientific reports, 8(1), 1506.Google Scholar
  27. 27.
    Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016). Binary ant lion approaches for feature selection. Neurocomputing, 213, 54–65.Google Scholar
  28. 28.
    Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.Google Scholar
  29. 29.
    Faris, H., Ala’M, A. Z., Heidari, A. A., Aljarah, I., Mafarja, M., Hassonah, M. A., & Fujita, H. (2019). An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks. Information Fusion, 48, 67–83.Google Scholar
  30. 30.
    Faris, H., Aljarah, I., Al-Betar, M.A., & Mirjalili, S. (2017). Grey wolf optimizer: A review of recent variants and applications. Neural Computing and Applications, 1–23.Google Scholar
  31. 31.
    Faris, H., Aljarah, I., Al-Madi, N., & Mirjalili, S. (2016). Optimizing the learning process of feedforward neural networks using lightning search algorithm. International Journal on Artificial Intelligence Tools, 25(06), 1650033.Google Scholar
  32. 32.
    Faris, H., Aljarah, I., & Al-Shboul, B. (2016). A hybrid approach based on particle swarm optimization and random forests for e-mail spam filtering. In International Conference on Computational Collective Intelligence (pp. 498–508). Springer, Cham.Google Scholar
  33. 33.
    Faris, H., Aljarah, I., & Mirjalili, S. (2016). Training feedforward neural networks using multi-verse optimizer for binary classification problems. Applied Intelligence, 45(2), 322–332.Google Scholar
  34. 34.
    Faris, H., Aljarah, I., & Mirjalili, S. (2017). Evolving radial basis function networks using moth–flame optimizer. In Handbook of Neural Computation (pp. 537–550).Google Scholar
  35. 35.
    Faris, H., Aljarah, I., & Mirjalili, S. (2017). Improved monarch butterfly optimization for unconstrained global search and neural network training. Applied Intelligence (pp. 1–20).Google Scholar
  36. 36.
    Faris, H., & Aljarah, I., et al. (2015). Optimizing feedforward neural networks using krill herd algorithm for e-mail spam detection. In 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (pp. 1–5). IEEE.Google Scholar
  37. 37.
    Faris, H., Hassonah, M. A., AlaM, A. Z., Mirjalili, S., & Aljarah, I. (2017). A multi-verse optimizer approach for feature selection and optimizing svm parameters based on a robust system architecture. Neural Computing and Applications, 1–15.Google Scholar
  38. 38.
    Faris, H., Mafarja, M. M., Heidari, A. A., Aljarah, I., AlaM, A. Z., Mirjalili, S., et al. (2018). An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowledge-Based Systems, 154, 43–67.Google Scholar
  39. 39.
    Gandomi, A. H., & Kashani, A. R. (2018). Construction cost minimization of shallow foundation using recent swarm intelligence techniques. IEEE Transactions on Industrial Informatics, 14(3), 1099–1106.Google Scholar
  40. 40.
    Green, R. C., Wang, L., & Alam, M. (2012). Training neural networks using central force optimization and particle swarm optimization: insights and comparisons. Expert Systems with Applications, 39(1), 555–563.Google Scholar
  41. 41.
    Gupta, S., Kumar, V., Rana, K., Mishra, P., & Kumar, J. (2016). Development of ant lion optimizer toolkit in labview. In 2016 International Conference onInnovation and Challenges in Cyber Security (ICICCS-INBUSH) (pp. 251–256).Google Scholar
  42. 42.
    Hamouda, E., El-Metwally, S., & Tarek, M. (2018). Ant lion optimization algorithm for kidney exchanges. PloS One, 13(5), e0196707.Google Scholar
  43. 43.
    Hansen, N., Müller, S. D., & Koumoutsakos, P. (2003). Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es). Evolutionary Computation, 11(1), 1–18.Google Scholar
  44. 44.
    Heidari, A. A., Faris, H., Aljarah, I., & Mirjalili, S. (2018). An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Computing, 1–18.Google Scholar
  45. 45.
    Heidari, A. A., Abbaspour, R. A. (2018). Enhanced chaotic grey wolf optimizer for real-world optimization problems: A comparative study. In Handbook of Research on Emergent Applications of Optimization Algorithms (pp. 693–727). IGI Global.Google Scholar
  46. 46.
    Heidari, A. A., Abbaspour, R. A., & Jordehi, A. R. (2017). An efficient chaotic water cycle algorithm for optimization tasks. Neural Computing and Applications, 28(1), 57–85.Google Scholar
  47. 47.
    Heidari, A. A., Abbaspour, R. A., & Jordehi, A. R. (2017). Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems. Applied Soft Computing, 57, 657–671.Google Scholar
  48. 48.
    Heidari, A. A., & Delavar, M. R. (2016). A modified genetic algorithm for finding fuzzy shortest paths in uncertain networks. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (pp. 299–304).Google Scholar
  49. 49.
    Heidari, A. A., & Pahlavani, P. (2017). An efficient modified grey wolf optimizer with lévy flight for optimization tasks. Applied Soft Computing, 60, 115–134.Google Scholar
  50. 50.
    Ho, Y. C., & Pepyne, D. L. (2002). Simple explanation of the no free lunch theorem of optimization. Cybernetics and Systems Analysis, 38(2), 292–298.MathSciNetzbMATHGoogle Scholar
  51. 51.
    Hu, P., Wang, Y., Wang, H., Zhao, R., Yuan, C., Zheng, Y., Lu, Q., Li, Y., & Masood, I. (2018). Alo-dm: A smart approach based on ant lion optimizer with differential mutation operator in big data analytics. In International Conference on Database Systems for Advanced Applications (pp. 64–73). Springer.Google Scholar
  52. 52.
    Hu, Y. C. (2014). Nonadditive similarity-based single-layer perceptron for multi-criteria collaborative filtering. Neurocomputing, 129, 306–314.Google Scholar
  53. 53.
    Igel, C., & Toussaint, M. (2003). On classes of functions for which no free lunch results hold. Information Processing Letters, 86(6), 317–321.MathSciNetzbMATHGoogle Scholar
  54. 54.
    Ilonen, J., Kamarainen, J. K., & Lampinen, J. (2003). Differential evolution training algorithm for feed-forward neural networks. Neural Processing Letters, 17(1), 93–105.Google Scholar
  55. 55.
    Kamboj, V. K., Bhadoria, A., & Bath, S. (2017). Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer. Neural Computing and Applications, 28(8), 2181–2192.Google Scholar
  56. 56.
    Karaboga, D., Akay, B., & Ozturk, C. (2007). Artificial bee colony (abc) optimization algorithm for training feed-forward neural networks. In International Conference on Modeling Decisions for Artificial Intelligence (pp. 318–329). Springer.Google Scholar
  57. 57.
    Kaushal, K., & Singh, S. (2017). Allocation of stocks in a portfolio using antlion algorithm: Investor’s perspective. IUP Journal of Applied Economics, 16(1), 34.Google Scholar
  58. 58.
    Kowalski, P. A., & Łukasik, S. (2016). Training neural networks with krill herd algorithm. Neural Processing Letters, 44(1), 5–17.Google Scholar
  59. 59.
    Krogh, A. (2008). What are artificial neural networks? Nature Biotechnology, 26(2), 195–197.Google Scholar
  60. 60.
    Lee, S., & Choeh, J. Y. (2014). Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Systems with Applications, 41(6), 3041–3046.Google Scholar
  61. 61.
    Li, Y., Feng, B., Li, G., Qi, J., Zhao, D., & Mu, Y. (2018). Optimal distributed generation planning in active distribution networks considering integration of energy storage. Applied Energy, 210, 1073–1081.Google Scholar
  62. 62.
    Lichman, M.: UCI machine learning repository (2013), http://archive.ics.uci.edu/ml
  63. 63.
    Mafarja, M., Aljarah, I., Heidari, A. A., Faris, H., Fournier-Viger, P., Li, X., & Mirjalili, S. (2018). Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-Based Systems, 161, 185–204.Google Scholar
  64. 64.
    Mafarja, M., Aljarah, I., Heidari, A. A., Hammouri, A. I., Faris, H., & AlaM, A. Z., et al. (2017). Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowledge-Based Systems.Google Scholar
  65. 65.
    Mallipeddi, R., Suganthan, P. N., Pan, Q. K., & Tasgetiren, M. F. (2011). Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing, 11(2), 1679–1696.Google Scholar
  66. 66.
    McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.MathSciNetzbMATHGoogle Scholar
  67. 67.
    Mirjalili, S. (2015). The ant lion optimizer. Advances in Engineering Software, 83, 80–98.Google Scholar
  68. 68.
    Mirjalili, S. (2015). How effective is the grey wolf optimizer in training multi-layer perceptrons. Applied Intelligence, 43(1), 150–161.Google Scholar
  69. 69.
    Mirjalili, S., Jangir, P., & Saremi, S. (2017). Multi-objective ant lion optimizer: A multi-objective optimization algorithm for solving engineering problems. Applied Intelligence, 46(1), 79–95.Google Scholar
  70. 70.
    Mirjalili, S. Z., Mirjalili, S., Saremi, S., Faris, H., & Aljarah, I. (2018). Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence, 48(4), 805–820.Google Scholar
  71. 71.
    Mirjalili, S. Z., Saremi, S., & Mirjalili, S. M. (2015). Designing evolutionary feedforward neural networks using social spider optimization algorithm. Neural Computing and Applications, 26(8), 1919–1928.Google Scholar
  72. 72.
    Nair, S. S., Rana, K., Kumar, V., & Chawla, A. (2017). Efficient modeling of linear discrete filters using ant lion optimizer. Circuits, Systems, and Signal Processing, 36(4), 1535–1568.Google Scholar
  73. 73.
    Ojha, V. K., Abraham, A., & Snášel, V. (2017). Metaheuristic design of feedforward neural networks: A review of two decades of research. Engineering Applications of Artificial Intelligence, 60, 97–116.Google Scholar
  74. 74.
    Oliva, D., Hinojosa, S., Elaziz, M.A., & Ortega-Sánchez, N. (2018). Context based image segmentation using antlion optimization and sine cosine algorithm. Multimedia Tools and Applications (pp. 1–37).Google Scholar
  75. 75.
    Petrović, M., Petronijević, J., Mitić, M., Vuković, N., Miljković, Z., & Babić, B. (2016). The ant lion optimization algorithm for integrated process planning and scheduling. In Applied Mechanics and Materials (vol. 834, pp. 187–192). Trans Tech Publ.Google Scholar
  76. 76.
    Rajan, A., Jeevan, K., & Malakar, T. (2017). Weighted elitism based ant lion optimizer to solve optimum var planning problem. Applied Soft Computing, 55, 352–370.Google Scholar
  77. 77.
    Raju, M., Saikia, L. C., & Sinha, N. (2016). Automatic generation control of a multi-area system using ant lion optimizer algorithm based pid plus second order derivative controller. International Journal of Electrical Power & Energy Systems, 80, 52–63.Google Scholar
  78. 78.
    Saxena, P., & Kothari, A. (2016). Ant lion optimization algorithm to control side lobe level and null depths in linear antenna arrays. AEU-International Journal of Electronics and Communications, 70(9), 1339–1349.Google Scholar
  79. 79.
    Schumacher, C., Vose, M. D., & Whitley, L. D. (2001). The no free lunch and problem description length. In Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (pp. 565–570). Morgan Kaufmann Publishers Inc.Google Scholar
  80. 80.
    Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L., & Greicius, M. D. (2009). Neurodegenerative diseases target large-scale human brain networks. Neuron, 62(1), 42–52.Google Scholar
  81. 81.
    Sexton, R. S., Dorsey, R. E., & Johnson, J. D. (1999). Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing. European Journal of Operational Research, 114(3), 589–601.zbMATHGoogle Scholar
  82. 82.
    Sexton, R. S., & Gupta, J. N. (2000). Comparative evaluation of genetic algorithm and backpropagation for training neural networks. Information Sciences, 129(1), 45–59.zbMATHGoogle Scholar
  83. 83.
    Shukri, S., Faris, H., Aljarah, I., Mirjalili, S., & Abraham, A. (2018). Evolutionary static and dynamic clustering algorithms based on multi-verse optimizer. Engineering Applications of Artificial Intelligence, 72, 54–66.Google Scholar
  84. 84.
    Siddique, M., & Tokhi, M. (2001) Training neural networks: backpropagation vs. genetic algorithms. In International Joint Conference on Neural Networks, 2001. Proceedings. IJCNN’01 (vol. 4, pp. 2673–2678). IEEE.Google Scholar
  85. 85.
    Slowik, A., & Bialko, M. (2008). Training of artificial neural networks using differential evolution algorithm. In 2008 Conference on Human System Interactions (pp. 60–65). IEEE.Google Scholar
  86. 86.
    Socha, K., & Blum, C. (2007). An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Computing and Applications, 16(3), 235–247.Google Scholar
  87. 87.
    Talatahari, S. (2016). Optimum design of skeletal structures using ant lion optimizer. Iran University of Science & Technology, 6(1), 13–25.Google Scholar
  88. 88.
    Tharwat, A., & Hassanien, A. E. (2018). Chaotic antlion algorithm for parameter optimization of support vector machine. Applied Intelligence, 48(3), 670–686.Google Scholar
  89. 89.
    Tian, T., Liu, C., Guo, Q., Yuan, Y., Li, W., & Yan, Q. (2018). An improved ant lion optimization algorithm and its application in hydraulic turbine governing system parameter identification. Energies, 11(1), 95.Google Scholar
  90. 90.
    Trivedi, I. N., Jangir, P., & Parmar, S. A. (2016). Optimal power flow with enhancement of voltage stability and reduction of power loss using ant-lion optimizer. Cogent Engineering, 3(1), 1208942.Google Scholar
  91. 91.
    Trujillo, M. C. R., Alarcón, T. E., Dalmau, O. S., & Ojeda, A. Z. (2017). Segmentation of carbon nanotube images through an artificial neural network. Soft Computing, 21(3), 611–625.Google Scholar
  92. 92.
    Wdaa, A. S. I. (2008). Differential evolution for neural networks learning enhancement. Ph.D. thesis, Universiti Teknologi Malaysia.Google Scholar
  93. 93.
    Whitley, D., Starkweather, T., & Bogart, C. (1990). Genetic algorithms and neural networks: Optimizing connections and connectivity. Parallel Computing, 14(3), 347–361.Google Scholar
  94. 94.
    Wienholt, W. (1993). Minimizing the system error in feedforward neural networks with evolution strategy. In ICANN93 (pp. 490–493). Springer.Google Scholar
  95. 95.
    Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82.Google Scholar
  96. 96.
    Wu, Z., Yu, D., & Kang, X. (2017). Parameter identification of photovoltaic cell model based on improved ant lion optimizer. Energy Conversion and Management, 151, 107–115.Google Scholar
  97. 97.
    Yamany, W., Tharwat, A., Hassanin, M. F., Gaber, T., Hassanien, A.E., & Kim, T. H. (2015). A new multi-layer perceptrons trainer based on ant lion optimization algorithm. In 2015 Fourth International Conference on Information Science and Industrial Applications (ISI) (pp. 40–45). IEEE.Google Scholar
  98. 98.
    Yao, P., & Wang, H. (2017). Dynamic adaptive ant lion optimizer applied to route planning for unmanned aerial vehicle. Soft Computing, 21(18), 5475–5488.Google Scholar
  99. 99.
    Yogarajan, G., & Revathi, T. (2018). Improved cluster based data gathering using ant lion optimization in wireless sensor networks. Wireless Personal Communications, 98(3), 2711–2731.Google Scholar
  100. 100.
    Yu, J., Wang, S., & Xi, L. (2008). Evolving artificial neural networks using an improved pso and dpso. Neurocomputing, 71(4), 1054–1060.Google Scholar
  101. 101.
    Zawbaa, H. M., Emary, E., & Grosan, C. (2016). Feature selection via chaotic antlion optimization. PloS One, 11(3), e0150652.Google Scholar
  102. 102.
    Zhang, J. R., Zhang, J., Lok, T. M., & Lyu, M. R. (2007). A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation, 185(2), 1026–1037.zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ali Asghar Heidari
    • 1
  • Hossam Faris
    • 2
  • Seyedali Mirjalili
    • 3
    Email author
  • Ibrahim Aljarah
    • 2
  • Majdi Mafarja
    • 4
  1. 1.School of Surveying and Geospatial EngineeringUniversity of TehranTehranIran
  2. 2.King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  3. 3.Institute of Integrated and Intelligent Systems, Griffith University, NathanBrisbaneAustralia
  4. 4.Faculty of Engineering and Technology, Department of Computer ScienceBirzeit UniversityBirzeitPalestine

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