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A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem

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Abstract

Meta-heuristics are problem-independent optimization techniques which provide an optimal solution by exploring and exploiting the entire search space iteratively. These techniques have been successfully engaged to solve distinct real-life and multidisciplinary problems. A good amount of literature has been already published on the design and role of various meta-heuristic algorithms and on their variants. The aim of this study is to present a comprehensive analysis of nature-inspired meta-heuristic utilized in the domain of feature selection. A systematic review methodology has been used for synthesis and analysis of one hundered and seventy six articles. It is one of the important multidisciplinary research areas that assist in finding an optimal set of features so that a better rate of classification can be achieved. The concept of feature selection process along with relevance and redundancy metric is briefly elucidated. A categorical list of nature-inspired meta-heuristic techniques has been presented. The major applications of these techniques are explored to highlight the least and most explored areas. The area of disease diagnosis has been extensively assessed. In addition, the special attention has been given on highlighting the role and performance of binary and chaotic variants of different nature-inspired meta-heuristic techniques. The summary of nature-inspired meta-heuristic methods and their variants along with datasets, performance (mean, best, worst, error rate and standard deviation) is also depicted. In addition, the detailed publication trend of meta-heuristic feature selection approaches has also been presented. The research gaps have been identified for the researcher who inclines to design or analyze the performance of divergent meta-heuristic techniques in solving feature selection problem.

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References

  1. Sevinç E, Coşar A (2010) An evolutionary genetic algorithm for optimization of distributed database queries. Comput J 54(5):717–725

    MATH  Google Scholar 

  2. Sharma M, Singh G, Singh R, Singh G (2015) Analysis of DSS queries using entropy based restricted genetic algorithm. Appl Math Inf Sci 9(5):2599

    Google Scholar 

  3. Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2015) Feature selection for high-dimensional data. Springer, Cham, pp 31–40

    Google Scholar 

  4. Gacquer D et al (2011) Comparative study of supervised classification algorithms for the detection of atmospheric pollution. Eng Appl Artif Intell 24(6):1070–1083

    Google Scholar 

  5. Zheng H, Zhang Y (2008) Feature selection for high-dimensional data in astronomy. Adv Space Res 41(12):1960–1964

    Google Scholar 

  6. Sharif M, Khan MA, Iqbal Z, Azam MF, Lali MIU, Javed MY (2018) Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Electron Agric 150:220–234

    Google Scholar 

  7. Ravisankar P, Ravi V, Rao GR, Bose I (2011) Detection of financial statement fraud and feature selection using data mining techniques. Decis Support Syst 50(2):491–500

    Google Scholar 

  8. Chaves R et al (2009) SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting. Neurosci Lett 461(3):293–297

    Google Scholar 

  9. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517

    Google Scholar 

  10. Wang L, Jinshou Y (2005) Fault feature selection based on modified binary PSO with mutation and its application in chemical process fault diagnosis. In: International conference on natural computation. Springer, Heidelberg

  11. Tang J, Huan L (2012) Feature selection with linked data in social media. In: Proceedings of the 2012 SIAM international conference on data mining. Society for industrial and applied mathematics

  12. Donoho D, Jin J (2008) Higher criticism thresholding: optimal feature selection when useful features are rare and weak. Proc Natl Acad Sci 105(39):14790–14795

    MATH  Google Scholar 

  13. Tayarani-N MH, Yao X, Xu H (2014) Meta-heuristic algorithms in car engine design: a literature survey. IEEE Trans Evol Comput 19(5):609–629

    Google Scholar 

  14. Shaheen AM, Spea SR, Farrag SM, Abido MA (2018) A review of meta-heuristic algorithms for reactive power planning problem. Ain Shams Eng J 9(2):215–231

    Google Scholar 

  15. Memeti S et al (2018) A review of machine learning and meta-heuristic methods for scheduling parallel computing systems. In: Proceedings of the international conference on learning and optimization algorithms: theory and applications. ACM

  16. Teoh CK, Wibowo A, Ngadiman MS (2015) Review of state of the art for metaheuristic techniques in academic scheduling problems. Artif Intell Rev 44(1):1–21

    Google Scholar 

  17. Kalra M, Singh S (2015) A review of metaheuristic scheduling techniques in cloud computing. Egypt Inf J 16(3):275–295

    Google Scholar 

  18. Feizollah A et al (2015) A review on feature selection in mobile malware detection. Dig Invest 13:22–37

    Google Scholar 

  19. Asghar MZ, Khan A, Ahmad S, Kundi FM (2014) A review of feature extraction in sentiment analysis. J Basic Appl Sci Res 4(3):181–186

    Google Scholar 

  20. Arora S, Singh H, Sharma M et al (2019) A new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection. IEEE Access 7:26343–26361

    Google Scholar 

  21. Koller D, Mehran S (1996) Toward optimal feature selection. Stanford InfoLab

  22. Saikat D, Suramanian C, Amit KD (2019) Machine Learning. First impression, Pearson

  23. Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5:1205–1224

    MathSciNet  MATH  Google Scholar 

  24. Stojanović I et al (2017) Application of heuristic and metaheuristic algorithms in solving constrained weber problem with feasible region bounded by arcs. In: Mathematical Problems in Engineering

  25. Hosny MI (2010) Investigating heuristic and meta-heuristic algorithms for solving pickup and delivery problems. Cardiff University, Cardiff

    Google Scholar 

  26. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734

    Google Scholar 

  27. Naghdiani M, Jahanshahi M (2017) GSO: a new solution for solving unconstrained optimization tasks using garter snake’s behavior. In: International conference on computational science and computational intelligence (CSCI)

  28. Faisal M, Hassan M, Mansour A (2016) AntStar: enhancing optimization problems byintegrating an ant system and A * algorithm. Sci Program 2016::5136327. http://dx.doi.org/10.1155/2016/5136327

    Google Scholar 

  29. Xu W et al (2016) An improved discrete bees algorithm for correlation-aware service aggregation optimization incloud manufacturing. Int J Adv Manufact Technol 84(1–4):17–28

    Google Scholar 

  30. Cuevas E, González A, Zaldívar D, Pérez-Cisneros M (2015) An optimisation algorithm based on the behaviour of locust swarms. Int J Bio-Inspir Comput 7(6):402–407

    Google Scholar 

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

    Google Scholar 

  32. Di Stefano A et al (2015) A4sdn-adaptive alienated ant algorithm for software-defined networking. In: 2015 10th International conference on P2P, parallel, grid, cloud and internet computing (3PGCIC). IEEE

  33. Mirjalili S (2015) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective discrete, and multiobjective problems [J]. Neural. Comput Appl 27(4):1053–1073

    MathSciNet  Google Scholar 

  34. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Google Scholar 

  35. Marinakis Y, Marinaki M, Matsatsinis N (2010) A bumble bees mating optimization algorithm for global unconstrained optimization problems. Nat Inspir Cooperative Strateg Optim 284:305–318

    MATH  Google Scholar 

  36. James JQ, Li VO (2015) A social spider algorithm for global optimization. Appl Soft Comput 30:614–627

    Google Scholar 

  37. Mohammad M-R (2014) Dispersive flies optimization. In: 2014 Federated conference on computer science and information systems, Warsaw, Poland

  38. Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26(2):69–74

    Google Scholar 

  39. Anandaraman C, Sankar AVM, Natarajan R (2012) A new evolutionary algorithm based on bacterial evolution and its applications for scheduling a flexible manufacturing system. J TeknikIndustri 14:1–12

    Google Scholar 

  40. Djenouri Y et al (2012) Bees swarm optimization for web association rule mining. In: IEEE/WIC/ACM International conferences on web intelligence and intelligent agent technology, vol. 3. IEEE

  41. Ben N, Hong W (2012) Bacterial colony optimization. Discrete Dyn Nat Soc 2012:1–28

    MathSciNet  MATH  Google Scholar 

  42. Mahamed GH, Omran, IM, Salah al-Sharhan, MK (2011) Stochastic diffusion search for continuous global optimization. In: International conference on swarm intelligence ICSI, Cergy, France

  43. Niknam T et al (2011) A modified honey bee mating optimization algorithm for multiobjective placement of algorithm for multiobjective placement of renewable energy resources. Appl Energy 88(12):4817–4830

    Google Scholar 

  44. Chen ZH, Yan TH (2010) Cockroach swarm optimization. In: 2010 2nd international conference on computer engineering and technology

  45. Bitam S, Batouche M, Talbi EG (2010) A survey on bee colony algorithms. In: 2010 IEEE international symposium on parallel & distributed processing, workshops and phd forum (ipdpsw). IEEE

  46. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization (NICSO 2010), vol 284. Studies in computational intelligence. Springer, Berlin

    Google Scholar 

  47. Feng X, Lau FCM, Gao D (2009) A new bio-inspired approach to the travelling salesman problem in Complex Sciences. Lect Notes Inst Comput Sci Soc Inf Telecommun Eng 5:1310–1321

    Google Scholar 

  48. Yang, X-S (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer, Heidelberg

  49. Garcia FJM, Pérez JA (2008) Jumping frogs optimization: a new swarm method for discrete optimization. In: DOCUMENTO DE TRABAJO–DEIOC 3/2008. Universidad Dela Laguna

  50. Krishnanand KN, Ghose D (2009) Glowworm swarm optimisation: a new method for optimising multi-modal functions. Int J Comput Intell Stud 1(1):93–119

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  52. Wedde HF, Farooq M, Zhang Y (2004) BeeHive: an efficient fault-tolerant routing algorithm inspired by honey bee behaviour. In: International workshop on Ant Colony optimization and swarm intelligence, Springer, Berlin

  53. Dorigo, M, Gianni DC (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 2. IEEE

  54. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Google Scholar 

  55. Harifi S, Khalilian M, Mohammadzadeh J, Ebrahimnejad S (2019) Emperor Penguins Colony: a new metaheuristic algorithm for optimization. Evol Intel 12(2):211–226

    Google Scholar 

  56. Heidari AA et al (2019) Harris hawks optimization: algorithm and applications. Future Gener computsyst 97:849–872

    Google Scholar 

  57. Lamy JB (2019) Artificial feeding birds (AFB): a new metaheuristic inspired by the behaviour of pigeons. Advances in nature-inspired computing and applications. Springer, Cham, pp 43–60

    Google Scholar 

  58. Zolghadr-Asli B, Bozorg-Haddad O, Chu X (2018) Crow search algorithm (CSA). In: Bozorg-Haddad O (ed) Advanced optimization by nature-inspired algorithms, vol 720. Studies in computational intelligence. Springer, Singapore, pp 143–149

    Google Scholar 

  59. Hosseini E (2017) Laying chicken algorithm: a new meta-heuristic approach to solve continuous programming problems. J Appl Computat Math 6(344):2

    Google Scholar 

  60. Brabazon A, Cui W, O’Neill M (2016) The raven roosting optimization algorithm. Soft Comput 20(2):525–545

    Google Scholar 

  61. Shen H, Zhu Y, Liang X (2014) Lifecycle-based swarm optimization method for numerical optimization. Discrete Dyn Nat Soc 2014:1–14

    Google Scholar 

  62. Barresi KM (2014) Foraging agent swarm optimization with applications in data clustering. In: International conference on swarm intelligence, ANTS, swarm intelligence, pp. 230–237

  63. Meng X et al (2014) A new bio-inspired algorithm: chicken swarm optimization. In: International conference in swarm intelligence. Springer, Cham

  64. Sur C, Shukla A (2013) New bio-inspired meta-heuristics: green herons optimization algorithm—for optimization of travelling salesman problem and road network. In: Panigrahi BK, Suganthan PN, Das S, Dash SS (eds) Swarm, evolutionary, and memetic computing, SEMCCO 2013, vol 8298. Lecture Notes in Computer Science. Springer, Cham, pp 168–179

    Google Scholar 

  65. Duman E, Uysal M, Alkaya AF (2012) Migrating Birds Optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inf Sci 217(25):65–77

    MathSciNet  Google Scholar 

  66. Yang XS, Deb S (2010) Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. In: Gonzalez JR et al (eds) Nature inspired cooperative strategies for optimization, vol 284. Springer, Berlin, pp 101–111

    Google Scholar 

  67. Yang X-S, Suash D (2009) Cuckoo search via Lévy flights. In: World congress on nature & biologically inspired computing. In: NaBIC 2009. IEEE

  68. Su A et al (2009) Dove swarm optimization algorithm. In: Bo X, Gao W-J (eds) Innovative computational intelligence: a rough guide to 134 Clever Algorithms. Springer, Berlin, pp 239–241

    Google Scholar 

  69. Eberhart R, James K (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol 4

  70. Dhiman G, Kumar V (2019) Spotted Hyena optimizer for solving complex and non-linear constrained engineering problems. In: Yadav N, Yadav A, Bansal J, Deep K, Kim J (eds) Harmony search and nature inspired optimization algorithms. Advances in Intelligent Systems and Computing, vol 741. Springer, Singapore, pp 857–867

    Google Scholar 

  71. Wang GG, Deb S, Coelho LS (2015) Elephant herding optimization. In: 2015 3rd international symposium on computational and business intelligence (ISCBI), Bali, Indonesia

  72. Yazdani M, Jolai F (2016) Lion Optimization Algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng 3(1):24–36

    Google Scholar 

  73. Ibrahim MK, Ali RS (2016) Novel optimization algorithm inspired by camel traveling behavior. Iraqi J Electr Electr Eng 12(2):167–177

    Google Scholar 

  74. Chen CC, Tsai YC, Liu II, Lai CC, Yeh YT, Kuo SY, Chou YH et al (2016) A novel metaheuristic: Jaguar Algorithm with learning behavior. In: IEEE international conference on systems, man, and cybernetics

  75. Deb S, Fong S, Tian Z et al (2015) Elephant search algorithm for optimization problems. In: Tenth international conference on digital information management (ICDIM)

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

    Google Scholar 

  77. Tilahun SL, Ong HC (2015) Prey-predator algorithm: a new metaheuristic algorithm for optimization problems. Int J Inform Technol Dec Mak 14(06):1331–1352

    Google Scholar 

  78. Odili JB, Kahar MNM, Anwar S (2015) African buffalo optimization: a swarm-intelligence technique. Proc Comput Sci 76:443–448

    Google Scholar 

  79. 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 

  80. Oftadeh R, Mahjoob MJ, Shariatpanahi M (2010) A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput Math Appl 60(7):2087–2098

    MATH  Google Scholar 

  81. Mucherino A, Onur S (2007) Monkey search: a novel meta-heuristic search for global optimization. In: AIP conference proceedings, vol 953.1

  82. Chu S-C, Tsai P-W, Pan J-S (2006) Cat swarm optimization. In: Pacific rim international conference on artificial intelligence. Springer, Berlin

  83. Shadravana S, Najib HR, Bardsiri VK (2019) The Sailfish Optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20–34

    Google Scholar 

  84. Haldar V, Chakraborty N (2017) A novel evolutionary technique based on electrolocation principle of elephant nose fish and shark: fish electrolocation optimization. Soft Comput 21(14):3827–3848

    Google Scholar 

  85. Bethiana N (2017) Killer whale algorithm: an algorithm inspired by the life of killer whale. Procedia Comput Sci 124:151–157

    Google Scholar 

  86. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  87. Neshat M, Sepidnam G, Sargolzaei M, Toosi AN (2014) Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif Intell Rev 42(4):965–997

    Google Scholar 

  88. Serani A, Diez M (2017) Dolphin pod optimization: a nature-inspired deterministic algorithm for simulation-based design. In: Book: machine learning, optimization, and big data: second international workshop, MOD 2017, Volterra, Italy, 2017, pp 14–17

  89. Hersovici M et al (1998) The shark-search algorithm. An application: tailored Web site mapping. Comput Netw ISDN Syst 30(1–7):317–326

    Google Scholar 

  90. Merrikh-Bayat F (2015) The runner-root algorithm. J Appl Soft Comput 33:292–303

    Google Scholar 

  91. Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation, UCNC 2012: unconventional computation and natural computation, vol 7445, pp 240–249

  92. Liuab Y, Liub J, Mac L, Tian L (2017) Artificial root foraging optimizer algorithm with hybrid strategies. Saudi J Biol Sci 24(2):268–275

    Google Scholar 

  93. Karimkashi S, Kishk AA (2010) Invasive weed optimization and its features in electromagnetics. IEEE Trans Antennas Propag 58(4):1269–1278

    Google Scholar 

  94. Uymaz SA, Tezel G, Yel E (2015) Artificial algae algorithm (AAA) for nonlinear global optimization. Appl Soft Comput 31:153–171

    Google Scholar 

  95. Zhang J, Xiao M, Gao L, Pan Q (2018) Queuing search algorithm: a novel metaheuristic algorithm for solving engineering optimization problems. Appl Math Model 63:464–490

    MathSciNet  MATH  Google Scholar 

  96. Fattahi E, Bidar M, Kanan HR (2018) Focus group: an optimization algorithm inspired by human behavior. Int J Comput Intell Appl 17(01):1–27

    Google Scholar 

  97. Jangir P, Parmar S, Trivedi I (2017) Human behavior based optimization algorithm for optimal power flow problem with discrete and continuous control variables. Int J Eng Technol Res Manag 1(2):26–35

    Google Scholar 

  98. Azar A, Seyedmirzaee S (2013) Providing new meta-heuristic algorithm for optimization problems inspired by humans’ behavior to improve their positions. Int J Artif Intell Appl 4(1):1–12

    Google Scholar 

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

    Google Scholar 

  100. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99

    Google Scholar 

  101. Zhang J, Zhou Y, Luo Q (2019) Nature-inspired approach: a wind-driven water wave optimization algorithm. Applied Intelligence 49(1):233–252

    Google Scholar 

  102. Hansen P, Mladenović N, Brimberg J, Pérez JAM (2019) Variable neighborhood search. In: Gendreau M, Potvin JY (eds) Handbook of metaheuristics, vol 272. International series in operations research & management science. Springer, Cham, pp 57–97

    Google Scholar 

  103. Wedyan A, Whalley J, Narayanan A (2017) Hydrological cycle algorithm for continuous optimization problems. J Optim 2017:1–25

    MathSciNet  MATH  Google Scholar 

  104. Hosseini F, Kaedi M (2018) A metaheuristic optimization algorithm inspired by the effect of sunlight on the leaf germination. Int J Appl Metaheuristic Comput 9(1):40–48

    Google Scholar 

  105. Sun G, Zhao R, Lan Y (2016) Joint operations algorithm for large-scale global optimization. Appl Soft Comput 38:1025–1039

    Google Scholar 

  106. Hajipour H, Rostami H, BehzadiKhourmuji H, Oskouei RJ et al (2013) ODMA: a new metaheuristic optimization algorithm based on open source development model. In: 2012 12th international conference on intelligent systems design and applications (ISDA) IEEE, Kochi, India

  107. Muthiah-Nakarajan V, Noel MM (2016) Galactic Swarm Optimization: a new global optimization metaheuristic inspired by galactic motion. Appl Soft Comput 38:771–787

    Google Scholar 

  108. Zou F, Chen D, Wang J (2016) An improved teaching-learning-based optimization with the social character of PSO for global optimization. Comput Intell Neurosci 2016(2):1–10

    Google Scholar 

  109. Chetty S, Adewumi AO (2015) A study on the enhanced best performance algorithm for the just-in-time scheduling problem. Discrete Dyn Nat Soc 2015:1–12

    MathSciNet  MATH  Google Scholar 

  110. Dash T, Sahu PK (2015) Gradient gravitational search: an efficient metaheuristic algorithm for global optimization. J Comput Chem 36(14):1060–1068

    Google Scholar 

  111. Li W, Wang L, Yao Q, Jiang Q, Yu L, Wang B, Hei X (2015) Cloud particles differential evolution algorithm: a novel optimization method for global numerical optimization. Math Prob Eng 2015:1–36

    Google Scholar 

  112. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Google Scholar 

  113. Kaveh A, Mahdavi VR (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27

    Google Scholar 

  114. Kaveh A, Share MAM, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech 224(1):85–107

    MATH  Google Scholar 

  115. Ibrahim A, Rahnamayan S, Martin MV (2014) Simulated raindrop algorithm for global optimization. In: 2014 IEEE 27th Canadian conference on electrical and computer engineering (CCECE). IEEE

  116. Abdechiri M, Meybodi MR, Bahrami H (2013) Gases Brownian motion optimization: an algorithm for optimization (GBMO). Appl Soft Comput 13(5):2932–2946

    Google Scholar 

  117. Taherdangkoo M, Paziresh M, Yazdi M, Bagheri MH (2013) An efficient algorithm for function optimization: modified stem cells algorithm. Cent Eur J Eng 3(1):36–50

    Google Scholar 

  118. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112:283–294

    Google Scholar 

  119. Shi Y (2011) Brainstorm optimization algorithm. In: International conference in swarm intelligence. Springer, Heidelberg

  120. Hamed SH (2011) Otsu’s criterion-based multilevel thresholding by a nature-inspired meta-heuristic called Galaxy-based Search Algorithm. In: NaBIC

  121. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    MATH  Google Scholar 

  122. Hosseini HS (2007) Problem-solving by intelligent water drops. In: 2007 IEEE congress on evolutionary computation. IEEE

  123. Chen MR, Lu YZ, Yang G (2007) Population-based extremal optimization with adaptive Lévy mutation for constrained optimization. In: Wang Y, Cheung Y, Liu H (eds) Computational intelligence and security. CIS 2006, vol 4456. Lecture notes in computer science. Springer, Berlin

    Google Scholar 

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

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  126. Emary E, Zawbaa HM, Hassanien AE (2016) Binary ant lion approaches for feature selection. Neurocomputing 213:54–65

    Google Scholar 

  127. Balasaraswathi VR, Sugumaran M, Hamid Y (2017) Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms. J Commun Inf Netw 2(4):107–119

    Google Scholar 

  128. Srivastava MS, Joshi MN, Gaur M (2014) A review paper on feature selection methodologies and their applications. IJCSNS 14(5):78

    Google Scholar 

  129. Subanya B, Rajalaxmi RR (2014) Artificial bee colony based feature selection for effective cardiovascular disease diagnosis. Int J Sci Eng Res 5(5):606–612

    Google Scholar 

  130. Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Google Scholar 

  131. Mafarja M et al (2019) Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Syst Appl 117:267–286

    Google Scholar 

  132. Sarhani M, El Afia A, Faizi R (2018) Facing the feature selection problem with a binary PSO-GSA approach. In: Recent developments in metaheuristics, pp 447–462. Springer, Cham

  133. Nakamura RYM, et al (2012) BBA: a binary bat algorithm for feature selection. In: 2012 25th SIBGRAPI conference on graphics, patterns and images. IEEE

  134. Rodrigues D, et al (2013) BCS: a binary cuckoo search algorithm for feature selection. In: 2013 IEEE international symposium on circuits and systems (ISCAS). IEEE

  135. Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31(1):171–188

    Google Scholar 

  136. Ewees AA, El Aziz MA, Hassanien AE (2019) Chaotic multi-verse optimizer-based feature selection. Neural Comput Appl 31(4):991–1006

    Google Scholar 

  137. Sayed GI, Darwish A, Hassanien AE (2018) A new chaotic whale optimization algorithm for features selection. J Classif 35(2):300–344

    MathSciNet  MATH  Google Scholar 

  138. Ahmed K, Hassanien AE, Bhattacharyya S (2017) A novel chaotic chicken swarm optimization algorithm for feature selection. In: 2017 Third international conference on research in computational intelligence and communication networks (ICRCICN). IEEE

  139. Nag K, Pal NR (2019) Genetic programming for classification and feature selection. Evolutionary and swarm intelligence algorithms. Springer, Cham, pp 119–141

    Google Scholar 

  140. Arora S, Anand P (2019) Binary butterfly optimization approaches for feature selection. Expert Syst Appl 116:147–160

    Google Scholar 

  141. Hussien AG, et al. (2019) S-shaped binary whale optimization algorithm for feature selection. In: Recent trends in signal and image processing. Springer, Singapore, pp 79–87

  142. Zakeri A, Hokmabadi A (2019) Efficient feature selection method using real-valued grasshopper optimization algorithm. Expert Syst Appl 119:61–72

    Google Scholar 

  143. Selvakumar B, Muneeswaran K (2019) Firefly algorithm based feature selection for network intrusion detection. Comput Secur 81:148–155

    Google Scholar 

  144. Jain R, Gupta D, Khanna A (2019) Usability feature optimization using MWOA. In: Bhattacharyya S, Hassanien A, Gupta D, Khanna A, Pan I (eds) International conference on innovative computing and communications, vol 56. Lecture notes in networks and systems. Springer, Singapore

    Google Scholar 

  145. Faris H et al (2018) A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture. Neural Comput Appl 30(8):2355–2369

    Google Scholar 

  146. Mafarja M et al (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl Based Syst 145:25–45

    Google Scholar 

  147. El Aziz MA, Hassanien AE (2018) Modified cuckoo search algorithm with rough sets for feature selection. Neural Comput Appl 29(4):925–934

    Google Scholar 

  148. Zawbaa HM et al (2018) Large-dimensionality small-instance set feature selection: a hybrid bio-inspired heuristic approach. Swarm Evolut Comput 42:29–42

    Google Scholar 

  149. Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441–453

    Google Scholar 

  150. Krömer P et al (2018) Optimal column subset selection for image classification by genetic algorithms. Ann Oper Res 265(2):205–222

    MathSciNet  MATH  Google Scholar 

  151. Papa JP et al (2011) Feature selection through gravitational search algorithm. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE

  152. Palanisamy S, Kanmani S (2012) Artificial bee colony approach for optimizing feature selection. Int J Comput Sci Issues (IJCSI) 9(3):432

    Google Scholar 

  153. Banati H, Bajaj M (2011) Fire fly based feature selection approach. Int J Comput Sci Issues (IJCSI) 8(4):473

    Google Scholar 

  154. Wang GG (2016) Chaotic cuckoo search. Soft Comput 20(9):3349–3362

    Google Scholar 

  155. Javidi MM, Emami N (2016) A hybrid search method of wrapper feature selection by chaos particle swarm optimization and local search. Turk J Electr Eng Comput Sci 24(5):3852–3861

    Google Scholar 

  156. Zawbaa HM, Emary E, Grosan C (2016) Feature selection via chaotic antlion optimization. PLoS ONE 11(3):e0150652

    Google Scholar 

  157. Mucherino A, Seref O (2007) Monkey search: a novel meta-heuristic search for global optimization. In: AIP conference proceedings, vol. 953. AIP

  158. Kong X et al. (2012) A novel paddy field algorithm based on pattern searchh method. In: 2012 International conference on information and automation (ICIA). IEEE

  159. Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355–366

    Google Scholar 

  160. Sur C, Sharma S, Shukla A (2013) Egyptian vulture optimization algorithm—a new nature-inspired meta-heuristics for knapsack problem. In: The 9th international conference on computing and information technology (IC2IT2013). Springer, Berlin

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

    Google Scholar 

  162. Li T, Fong S (2019) A fast feature selection method based on coefficient of variation for diabetics prediction using machine learning. Int J Extreme Autom Connect Healthcare 1(1):55–65

    Google Scholar 

  163. Xu Y, Cui Z, Zeng J (2010) Social-emotional optimization algorithm for non-linear constrained optimization problems. In: International conference on swarm, evolutionary, and memetic computing. Springer, Berlin

  164. Skinner JE, Molnar M, Vybiral T, Mitra M (1992) Application of chaos theory to biology and medicine. Integr Physiol Behav Sci 27:39–53

    Google Scholar 

  165. Denton TA, Diamond GA, Helfant RH, Khan S, Karagueuzian H (1990) Fascinating rhythm: a primer on chaos theory and its application to cardiology. Am Heart J 120(6):1419–1440

    Google Scholar 

  166. Ayers S (1997) The application of chaos theory to psychology. Theory Psychol 7(3):373–398

    Google Scholar 

  167. Stapleton D, Hanna JB, Ross JR (2006) Enhancing supply chain solutions with the application of chaos theory. Supply Chain Manag Int J 11(2):108–114

    Google Scholar 

  168. Sivakumar B (2000) Chaos theory in hydrology: important issues and interpretations. J Hydrol 227(1-4):1–20

    Google Scholar 

  169. Frazier C, Kockelman KM (2004) Chaos theory and transportation systems: instructive example. Transp Res Rec 1897(1):9–17

    Google Scholar 

  170. Gandomi AH, Yang XS (2014) Chaotic bat algorithm. J Comput Sci 5(2):224–232

    MathSciNet  Google Scholar 

  171. Mitića M, Vukovićb N, Petrovića M, Miljković Z (2015) Chaotic fruit fly optimization algorithm. Knowl Based Syst 89:446–458

    Google Scholar 

  172. Wang G-G, et al (2018) A novel metaheuristic algorithm inspired by rhino herd behaviour. In: Proceedings of The 9th EUROSIM congress on modelling and simulation, EUROSIM 2016, The 57th SIMS conference on simulation and modelling SIMS 2016. Linköping University Electronic Press

  173. Nogueira S, Sechidis K, Brown G (2017) On the stability of feature selection algorithms. J Mach Learn Res 18(1):6345–6398

    MathSciNet  MATH  Google Scholar 

  174. Dunne K, Cunningham P, Azuaje F (2002) Solutions to instability problems with sequential wrapper-based approaches to feature selection (technical note). Department of Computer Science, Trinity College, University of Dublin; 2002. Jan. Report No. TCD-CS-2002-28

  175. Wald R, Khoshgoftaar TM, Napolitano A (2013) Stability of filter-and wrapper-based feature subset selection. In: 2013 IEEE 25th international conference on tools with artificial intelligence. IEEE

  176. Goh WWB, Wong L (2016) Evaluating feature-selection stability in next-generation proteomics. J Bioinform Comput Biol 14(05):1650029

    Google Scholar 

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Sharma, M., Kaur, P. A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem. Arch Computat Methods Eng 28, 1103–1127 (2021). https://doi.org/10.1007/s11831-020-09412-6

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