Moth–flame optimization algorithm: variants and applications

Abstract

This paper thoroughly presents a comprehensive review of the so-called moth–flame optimization (MFO) and analyzes its main characteristics. MFO is considered one of the promising metaheuristic algorithms and successfully applied in various optimization problems in a wide range of fields, such as power and energy systems, economic dispatch, engineering design, image processing and medical applications. This manuscript describes the available literature on MFO, including its variants and hybridization, the growth of MFO publications, MFO application areas, theoretical analysis and comparisons of MFO with other algorithms. Conclusions focus on the current work on MFO, highlight its weaknesses, and suggest possible future research directions. Researchers and practitioners of MFO belonging to different fields, like the domains of optimization, medical, engineering, clustering and data mining, among others will benefit from this study.

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References

  1. 1.

    Abdel-mawgoud H, Kamel S, Ebeed M, Youssef AR (2017) Optimal allocation of renewable dg sources in distribution networks considering load growth. In: 2017 nineteenth international middle east power systems conference (MEPCON). IEEE, pp 1236–1241

  2. 2.

    Abdel-mawgoud H, Kamel S, Tostado M, Yu J, Jurado F (2018) Optimal installation of multiple dg using chaotic moth-flame algorithm and real power loss sensitivity factor in distribution system. In: 2018 international conference on smart energy systems and technologies (SEST), IEEE. pp 1–5

  3. 3.

    Abdelmadjid C, Mohamed SA, Boussad B (2013) Cfd analysis of the volute geometry effect on the turbulent air flow through the turbocharger compressor. Energy Procedia 36:746–755

    Google Scholar 

  4. 4.

    Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Google Scholar 

  5. 5.

    Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435

    Google Scholar 

  6. 6.

    Abualigah LM, Khader AT, Hanandeh ES (2018a) A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125

    Google Scholar 

  7. 7.

    Abualigah LM, Khader AT, Hanandeh ES (2018b) A hybrid strategy for krill herd algorithm with harmony search algorithm to improve the data clustering 1. Intell Decis Technol 12(1):3–14

    Google Scholar 

  8. 8.

    Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin

    Google Scholar 

  9. 9.

    Acharyulu B, Mohanty B, Hota P (2019) Comparative performance analysis of pid controller with filter for automatic generation control with moth-flame optimization algorithm. In: Applications of artificial intelligence techniques in engineering. Springer, pp 509–518

  10. 10.

    Adeec U (2000) Time complexity of genetic algorithms on exponentially scaled problems. Urbana 51:61–801

    Google Scholar 

  11. 11.

    Allam D, Yousri D, Eteiba M (2016) Parameters extraction of the three diode model for the multi-crystalline solar cell/module using moth-flame optimization algorithm. Energy Convers Manag 123:535–548

    Google Scholar 

  12. 12.

    Amini S, Homayouni S, Safari A, Darvishsefat AA (2018) Object-based classification of hyperspectral data using random forest algorithm. Geo-spatial Inf Sci 21(2):127–138

    Google Scholar 

  13. 13.

    Anbarasan P, Jayabarathi T (2017) Optimal reactive power dispatch using moth-flame optimization algorithm. Int J Appl Eng Res 12(13):3690–3701

    Google Scholar 

  14. 14.

    Anfal M, Abdelhafid H (2017) Optimal placement of PMUS in Algerian network using a hybrid particle swarm-moth flame optimizer (PSO-MFO). Electroteh Electron Autom 65(3):191–196

    Google Scholar 

  15. 15.

    Bai Q (2010) Analysis of particle swarm optimization algorithm. Comput Inf Sci 3(1):180

    Google Scholar 

  16. 16.

    Bajpai P, Kumar M (2010) Genetic algorithm-an approach to solve global optimization problems. Indian J Comput Sci Eng 1(3):199–206

    Google Scholar 

  17. 17.

    Barham R, Sharieh A, Sleit A (2018) Moth flame optimization based on golden section search and its application for link prediction problem. Mod Appl Sci 13(1):10–27

    Google Scholar 

  18. 18.

    Bentouati B, Chaib L, Chettih S (2016) Optimal power flow using the moth flam optimizer: a case study of the Algerian power system. Indones J Electr Eng Comput Sci 1(3):431–445

    Google Scholar 

  19. 19.

    Bhadoria A, Kamboj VK, Sharma M, Bath S (2018) A solution to non-convex/convex and dynamic economic load dispatch problem using moth flame optimizer. INAE Lett 3(2):65–86

    Google Scholar 

  20. 20.

    Bhesdadiya R, Trivedi IN, Jangir P, Kumar A, Jangir N, Totlani R (2017) A novel hybrid approach particle swarm optimizer with moth-flame optimizer algorithm. In: Advances in computer and computational sciences. Springer, pp 569–577

  21. 21.

    Bhesdadiya R, Trivedi IN, Jangir P, Jangir N (2018) Moth-flame optimizer method for solving constrained engineering optimization problems. In: Advances in computer and computational sciences. Springer, pp 61–68

  22. 22.

    Blum C, Li X (2008) Swarm intelligence in optimization. In: Swarm intelligence. Springer, pp 43–85

  23. 23.

    Buch H, Trivedi IN, Jangir P (2017) Moth flame optimization to solve optimal power flow with non-parametric statistical evaluation validation. Cogent Eng 4(1):528–542

    Google Scholar 

  24. 24.

    Canito J, Ramos P, Moro S, Rita P (2018) Unfolding the relations between companies and technologies under the big data umbrella. Comput Ind 99:1–8

    Google Scholar 

  25. 25.

    Ceylan H, Ceylan H (2009) Harmony search algorithm for transport energy demand modeling. In: Music-inspired harmony search algorithm. Springer, pp 163–172

  26. 26.

    Ceylan O (2016) Harmonic elimination of multilevel inverters by moth-flame optimization algorithm. In: 2016 international symposium on industrial electronics (INDEL). IEEE, pp 1–5

  27. 27.

    Ceylan O, Paudyal S (2017) Optimal capacitor placement and sizing considering load profile variations using moth-flame optimization algorithm. In: 2017 international conference on modern power systems (MPS). IEEE, pp 1–6

  28. 28.

    Chauhan SS, Kotecha P (2016) Single level production planning in petrochemical industries using moth-flame optimization. In: 2016 IEEE region 10 conference (TENCON). IEEE, pp 263–266

  29. 29.

    Darwish A (2018) Bio-inspired computing: algorithms review, deep analysis, and the scope of applications. Future Comput Inform J 3(2):231–246

    MathSciNet  Google Scholar 

  30. 30.

    Das A, Mandal D, Ghoshal S, Kar R (2018) Concentric circular antenna array synthesis for side lobe suppression using moth flame optimization. AEU-Int J Electron Commun 86:177–184

    Google Scholar 

  31. 31.

    Dhiman R (2018) Moth-flame optimization technique for optimal coordination of directional overcurrent relay system. Ph.D. thesis

  32. 32.

    Dhyani A, Panda MK, Jha B (2018) Moth-flame optimization-based fuzzy-pid controller for optimal control of active magnetic bearing system. Iran J Sci Technol Trans Electr Eng 42(4):451–463

    Google Scholar 

  33. 33.

    Diab AAZ, Rezk H (2019) Optimal sizing and placement of capacitors in radial distribution systems based on grey wolf, dragonfly and moth-flame optimization algorithms. Iran J Sci Technol Trans Electr Eng 43(1):77–96

    Google Scholar 

  34. 34.

    Du P, Wang J, Yang W, Niu T (2019) A novel hybrid model for short-term wind power forecasting. Applied Soft Computing 39(1):93–106

    Google Scholar 

  35. 35.

    Ebrahim M, Becherif M, Abdelaziz AY (2018) Dynamic performance enhancement for wind energy conversion system using moth-flame optimization based blade pitch controller. Sustain Energy Technol Assess 27:206–212

    Google Scholar 

  36. 36.

    Eiben AE, Smit SK (2011) Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol Comput 1(1):19–31

    Google Scholar 

  37. 37.

    El Aziz MA, Ewees AA, Hassanien AE (2017) Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256

    Google Scholar 

  38. 38.

    Elsakaan AA, El-Sehiemy RA, Kaddah SS, Elsaid MI (2018a) An enhanced moth-flame optimizer for solving non-smooth economic dispatch problems with emissions. Energy 157:1063–1078

    Google Scholar 

  39. 39.

    Elsakaan AA, El-Sehiemy RAA, Kaddah SS, Elsaid MI (2018b) Economic power dispatch with emission constraint and valve point loading effect using moth flame optimization algorithm. Adv Eng Forum Trans Tech Publ 28:139–149

    Google Scholar 

  40. 40.

    Ewees AA, Sahlol AT, Amasha MA (2017) A bio-inspired moth-flame optimization algorithm for Arabic handwritten letter recognition. In: 2017 international conference on control, artificial intelligence, robotics & optimization (ICCAIRO). IEEE, pp 154–159

  41. 41.

    Faris H, Aljarah I, Mirjalili S (2017) Evolving radial basis function networks using moth–flame optimizer. In: Handbook of neural computation, vol 28. Elsevier, pp 537–550

  42. 42.

    Fausto F, Cuevas E, Valdivia A, González A (2017) A global optimization algorithm inspired in the behavior of selfish herds. Biosystems 160:39–55

    Google Scholar 

  43. 43.

    Gaston KJ, Bennie J, Davies TW, Hopkins J (2013) The ecological impacts of nighttime light pollution: a mechanistic appraisal. Biol Rev 88(4):912–927

    Google Scholar 

  44. 44.

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

    Google Scholar 

  45. 45.

    Gholizadeh S, Davoudi H, Fattahi F (2017) Design of steel frames by an enhanced moth-flame optimization algorithm. Steel Compos Struct 24(1):129–140

    Google Scholar 

  46. 46.

    Glover F (1977) Heuristics for integer programming using surrogate constraints. Decis Sci 8(1):156–166

    Google Scholar 

  47. 47.

    Gope S, Dawn S, Goswami AK, Tiwari PK (2016) Moth flame optimization based optimal bidding strategy under transmission congestion in deregulated power market. In: 2016 IEEE region 10 conference (TENCON). IEEE, pp 617–621

  48. 48.

    Gope S, Dawn S, Goswami AK, Tiwari PK (2016) Profit maximization with integration of wind farm in contingency constraint deregulated power market using moth flame optimization algorithm. In: 2016 IEEE region 10 conference (TENCON). IEEE, pp 1462–1466

  49. 49.

    Guo L, Wang GG, Wang H, Wang D (2013) An effective hybrid firefly algorithm with harmony search for global numerical optimization. Sci World J 13:30–44

    Google Scholar 

  50. 50.

    Guvenc U, Duman S, Hınıslıoglu Y (2017) Chaotic moth swarm algorithm. In: 2017 IEEE international conference on innovations in intelligent systems and applications (INISTA). IEEE, pp 90–95

  51. 51.

    Hassanien AE, Gaber T, Mokhtar U, Hefny H (2017) An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Comput Electron Agric 136:86–96

    Google Scholar 

  52. 52.

    Hazir E, Erdinler ES, Koc KH (2018) Optimization of cnc cutting parameters using design of experiment (doe) and desirability function. J For Res 29(5):1423–1434

    Google Scholar 

  53. 53.

    Heidari A, Moayedi A, Abbaspour RA (2017) Estimating origin-destination matrices using an efficient moth flame-based spatial clustering approach. Int Arch Photogram Rem Sens Spatial Inf Sci 42:102–112

    Google Scholar 

  54. 54.

    Holland J (1975) Adaptation in natural and artificial systems: an introductory analysis with application to biology. Control Artif Intell 3:1–15

    Google Scholar 

  55. 55.

    Huang L, Yang B, Zhang X, Yin L, Yu T, Fang Z (2019) Optimal power tracking of doubly fed induction generator-based wind turbine using swarm moth-flame optimizer. Trans Inst Meas Control 41(6):1491–1503

    Google Scholar 

  56. 56.

    Jain P, Saxena A (2019) An opposition theory enabled moth flame optimizer for strategic bidding in uniform spot energy market. Eng Sci Technol Int J

  57. 57.

    Jangir N, Pandya MH, Trivedi IN, Bhesdadiya R, Jangir P, Kumar A (2016) Moth-flame optimization algorithm for solving real challenging constrained engineering optimization problems. In: 2016 IEEE students’ conference on electrical, electronics and computer science (SCEECS). IEEE, pp 1–5

  58. 58.

    Jangir P (2017) Optimal power flow using a hybrid particle swarm optimizer with moth flame optimizer. Global J Res Eng 17:524–542

    Google Scholar 

  59. 59.

    Kamalapathi K, Priyadarshi N, Padmanaban S, Holm-Nielsen J, Azam F, Umayal C, Ramachandaramurthy V (2018) A hybrid moth-flame fuzzy logic controller based integrated cuk converter fed brushless dc motor for power factor correction. Electronics 7(11):288

    Google Scholar 

  60. 60.

    Kaur N, Rattan M, Gill SS (2018) Performance optimization of broadwell-y shaped transistor using artificial neural network and moth-flame optimization technique. Majlesi J Electr Eng 12(1):61–69

    Google Scholar 

  61. 61.

    Kennedy J (2010) Particle swarm optimization. Encycl Mach Learn 12:760–766

    Google Scholar 

  62. 62.

    Khalilpourazari S, Khalilpourazary S (2019) An efficient hybrid algorithm based on water cycle and moth-flame optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Comput 23(5):1699–1722

    Google Scholar 

  63. 63.

    Khalilpourazari S, Pasandideh SHR (2017) Multi-item EOQ model with nonlinear unit holding cost and partial backordering: moth-flame optimization algorithm. J Ind Prod Eng 34(1):42–51

    Google Scholar 

  64. 64.

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

    MathSciNet  MATH  Google Scholar 

  65. 65.

    Koziel S, Yang XS (2011) Computational optimization, methods and algorithms, vol 356. Springer, Berlin

    Google Scholar 

  66. 66.

    Kulturel-Konak S, Smith AE, Coit DW (2003) Efficiently solving the redundancy allocation problem using tabu search. IIE Trans 35(6):515–526

    Google Scholar 

  67. 67.

    Lai X, Qiao D, Zheng Y, Zhou L (2018) A fuzzy state-of-charge estimation algorithm combining ampere-hour and an extended kalman filter for li-ion batteries based on multi-model global identification. Appl Sci 8(11):2028

    Google Scholar 

  68. 68.

    Li C, Li S, Liu Y (2016a) A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting. Appl Intell 45(4):1166–1178

    Google Scholar 

  69. 69.

    Li WK, Wang WL, Li L (2018) Optimization of water resources utilization by multi-objective moth-flame algorithm. Water Resour Manag 32:3303–3316

    Google Scholar 

  70. 70.

    Li Y, Li X, Liu J, Ruan X (2019) An improved bat algorithm based on lévy flights and adjustment factors. Symmetry 11(7):925

    Google Scholar 

  71. 71.

    Li Z, Zhou Y, Zhang S, Song J (2016b) Lévy-flight moth-flame algorithm for function optimization and engineering design problems. Math Probl Eng 16:1–23

    Google Scholar 

  72. 72.

    Liu Y, Wang G, Chen H, Dong H, Zhu X, Wang S (2011) An improved particle swarm optimization for feature selection. J Bionic Eng 8(2):191–200

    Google Scholar 

  73. 73.

    Luo J, Chen H, Xu Y, Huang H, Zhao X et al (2018) An improved grasshopper optimization algorithm with application to financial stress prediction. Appl Math Model 64:654–668

    MathSciNet  MATH  Google Scholar 

  74. 74.

    Mei RNS, Sulaiman MH, Mustaffa Z, Daniyal H (2017) Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Appl Soft Comput 59:210–222

    Google Scholar 

  75. 75.

    Mei RNS, Sulaiman MH, Daniyal H, Mustaffa Z (2018) Application of moth-flame optimizer and ant lion optimizer to solve optimal reactive power dispatch problems. J Telecommun Electron Comput Eng 10(1–2):105–110

    Google Scholar 

  76. 76.

    Mekhamer S, Abdelaziz A, Badr M, Algabalawy M (2015) Optimal multi-criteria design of hybrid power generation systems: a new contribution. Int J Comput Appl 129(2):13–24

    Google Scholar 

  77. 77.

    Milad A (2013) Harmony search algorithm: strengths and weaknesses. J Comput Eng Inf Technol 2(1):1–7

    Google Scholar 

  78. 78.

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

    Google Scholar 

  79. 79.

    Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Google Scholar 

  80. 80.

    Mohamed AAA, Mohamed YS, El-Gaafary AA, Hemeida AM (2017) Optimal power flow using moth swarm algorithm. Electr Power Syst Res 142:190–206

    Google Scholar 

  81. 81.

    Mohanty B (2018) Performance analysis of moth flame optimization algorithm for agc system. Int J Model Simul 4(2):1–15

    Google Scholar 

  82. 82.

    Mohanty B, Acharyulu B, Hota P (2018) Moth-flame optimization algorithm optimized dual-mode controller for multiarea hybrid sources AGC system. Opt Control Appl Methods 39(2):720–734

    MathSciNet  MATH  Google Scholar 

  83. 83.

    Mostafa E, Abdel-Nasser M, Mahmoud K (2017) Performance evaluation of metaheuristic optimization methods with mutation operators for combined economic and emission dispatch. In: 2017 nineteenth international middle east power systems conference (MEPCON). IEEE, pp 1004–1009

  84. 84.

    Muangkote N, Sunat K, Chiewchanwattana S (2016) Multilevel thresholding for satellite image segmentation with moth-flame based optimization. In: 2016 13th international joint conference on computer science and software engineering (JCSSE). IEEE, pp 1–6

  85. 85.

    Murata T, Ishibuchi H, Tanaka H (1996) Multi-objective genetic algorithm and its applications to flowshop scheduling. Comput Ind Eng 30(4):957–968

    Google Scholar 

  86. 86.

    Nanda SJ et al (2016) Multi-objective moth flame optimization. In: 2016 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 2470–2476

  87. 87.

    Ouaarab A, Ahiod B, Yang XS (2014) Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput Appl 24(7–8):1659–1669

    Google Scholar 

  88. 88.

    Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm intell 1(1):33–57

    Google Scholar 

  89. 89.

    Reddy S, Panwar LK, Panigrahi BK, Kumar R (2018) Solution to unit commitment in power system operation planning using binary coded modified moth flame optimization algorithm (BMMFOA): a flame selection based computational technique. J Comput Sci 25:298–317

    MathSciNet  Google Scholar 

  90. 90.

    Reeves CR (1993) Improving the efficiency of tabu search for machine sequencing problems. J Oper Res Soc 44(4):375–382

    MATH  Google Scholar 

  91. 91.

    Sahu A, Hota SK (2018) Performance comparison of 2-DOF PID controller based on moth-flame optimization technique for load frequency control of diverse energy source interconnected power system. In: 2018 technologies for smart-city energy security and power (ICSESP). IEEE, pp 1–6

  92. 92.

    Said S, Mostafa A, Houssein EH, Hassanien AE, Hefny H (2017) Moth-flame optimization based segmentation for MRI liver images. In: International conference on advanced intelligent systems and informatics. Springer, pp 320–330

  93. 93.

    Saikia LC, Saha D (2016) Automatic generation control in competitive market conditions with moth-flame optimization based cascade controller. In: 2016 IEEE region 10 conference (TENCON). IEEE, pp 734–738

  94. 94.

    Saleh AA, Mohamed AAA, Hemeida AM, Ibrahim AA (2018) Comparison of different optimization techniques for optimal allocation of multiple distribution generation. In: 2018 international conference on innovative trends in computer engineering (ITCE). IEEE, pp 317–323

  95. 95.

    Salgotra R, Singh U, Saha S (2018) New cuckoo search algorithms with enhanced exploration and exploitation properties. Expert Syst Appl 95:384–420

    Google Scholar 

  96. 96.

    Sapre S, Mini S (2018) Optimized relay nodes positioning to achieve full connectivity in wireless sensor networks. Wirel Pers Commun 11(4):1–20

    Google Scholar 

  97. 97.

    Sarma A, Bhutani A, Goel L (2017) Hybridization of moth flame optimization and gravitational search algorithm and its application to detection of food quality. In: 2017 intelligent systems conference (IntelliSys). IEEE, pp 52–60

  98. 98.

    Abd el sattar S, Kamel S, Ebeed M (2016) Enhancing security of power systems including SSSC using moth-flame optimization algorithm. In: 2016 eighteenth international middle east power systems conference (MEPCON). IEEE, pp 797–802

  99. 99.

    Saurav S, Gupta VK, Mishra SK (2017) Moth-flame optimization based algorithm for facts devices allocation in a power system. In: 2017 international conference on innovations in information, embedded and communication systems (ICIIECS). IEEE, pp 1–7

  100. 100.

    Savsani V, Tawhid MA (2017) Non-dominated sorting moth flame optimization (ns-mfo) for multi-objective problems. Eng Appl Artif Intell 63:20–32

    Google Scholar 

  101. 101.

    Sayed GI, Hassanien AE (2018) A hybrid SA-MFO algorithm for function optimization and engineering design problems. Complex Intell Syst 4(3):195–212

    Google Scholar 

  102. 102.

    Sayed GI, Hassanien AE, Nassef TM, Pan JS (2016a) Alzheimer’s disease diagnosis based on moth flame optimization. In: International conference on genetic and evolutionary computing. Springer, pp 298–305

  103. 103.

    Sayed GI, Soliman M, Hassanien AE (2016b) Bio-inspired swarm techniques for thermogram breast cancer detection. In: Medical imaging in clinical applications, vol 4. Springer, pp 487–506

  104. 104.

    Shah YA, Habib HA, Aadil F, Khan MF, Maqsood M, Nawaz T (2018) Camonet: moth-flame optimization (MFO) based clustering algorithm for vanets. IEEE Access 6:48611–48624

    Google Scholar 

  105. 105.

    Shambour MKY (2019) Adaptive multi-crossover evolutionary algorithm for real-world optimisation problems. Int J Reason-Based Intell Syst 11(1):1–10

    Google Scholar 

  106. 106.

    Shehab M, Khader AT, Al-Betar M (2016) New selection schemes for particle swarm optimization. IEEJ Trans Electron Inf Syst 136(12):1706–1711

    Google Scholar 

  107. 107.

    Shehab M, Khader AT, Al-Betar MA (2017a) A survey on applications and variants of the cuckoo search algorithm. Appl Soft Comput 61:1041–1059

    Google Scholar 

  108. 108.

    Shehab M, Khader AT, Al-Betar MA, Abualigah LM (2017b) Hybridizing cuckoo search algorithm with hill climbing for numerical optimization problems. In: 2017 8th international conference on information technology (ICIT). IEEE, pp 36–43

  109. 109.

    Shehab M, Khader AT, Laouchedi M (2017c) Modified cuckoo search algorithm for solving global optimization problems. In: International conference of reliable information and communication technology. Springer, pp 561–570

  110. 110.

    Shehab M, Khader A, Laouchedi M (2018a) A hybrid method based on cuckoo search algorithm for global optimization problems. J Inf Commun Technol 17(3):469–491

    Google Scholar 

  111. 111.

    Shehab M, Khader AT, Laouchedi M, Alomari OA (2018b) Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization. J Supercomput 75:1–28

    Google Scholar 

  112. 112.

    Shehab M, Daoud MS, AlMimi HM, Abualigah LM, Khader AT (2019a) Hybridizing cuckoo search algorithm for extracting the ODF maxima in spherical harmonic representation. Int J Bio-Inspired Comput (in press)

  113. 113.

    Shehab M, Khader AT, Alia MA (2019b) Enhancing cuckoo search algorithm by using reinforcement learning for constrained engineering optimization problems. In: 2019 IEEE Jordan international joint conference on electrical engineering and information technology (JEEIT). IEEE, pp 812–816

  114. 114.

    Singh P, Prakash S (2017) Optical network unit placement in fiber-wireless (fiwi) access network by moth-flame optimization algorithm. Optical Fiber Technol 36:403–411

    Google Scholar 

  115. 115.

    Singh U, Singh SN (2019) A new optimal feature selection scheme for classification of power quality disturbances based on ant colony framework. Appl Soft Comput 74:216–225

    Google Scholar 

  116. 116.

    Smith T, Villet M (2001) Parasitoids associated with the diamondback moth, plutella xylostella (l.), in the eastern cape, South Africa. In: The management of diamondback moth and other crucifer pests. Proceedings of the fourth international workshop, pp 249–253

  117. 117.

    Soliman GM, Khorshid MM, Abou-El-Enien TH (2016) Modified moth-flame optimization algorithms for terrorism prediction. Int J Appl Innov Eng Manag 5:47–58

    Google Scholar 

  118. 118.

    Strumberger I, Sarac M, Markovic D, Bacanin N (2018) Moth search algorithm for drone placement problem. Int J Comput 3:75–80

    Google Scholar 

  119. 119.

    Sulaiman M, Mustaffa Z, Aliman O, Daniyal H, Mohamed M (2016) Application of moth-flame optimization algorithm for solving optimal reactive power dispatch problem 14(2):720–734

  120. 120.

    Tolba MA, Diab AAZ, Tulsky VN, Abdelaziz AY (2018) Lvci approach for optimal allocation of distributed generations and capacitor banks in distribution grids based on moth-flame optimization algorithm. Electr Eng 100(3):2059–2084

    Google Scholar 

  121. 121.

    Trivedi I, Kumar A, Ranpariya AH, Jangir P (2016) Economic load dispatch problem with ramp rate limits and prohibited operating zones solve using Levy flight moth-flame optimizer. In: 2016 international conference on energy efficient technologies for sustainability (ICEETS). IEEE, pp 442–447

  122. 122.

    Trivedi IN, Jangir P, Parmar SA, Jangir N (2018) Optimal power flow with voltage stability improvement and loss reduction in power system using moth-flame optimizer. Neural Comput Appl 30(6):1889–1904

    Google Scholar 

  123. 123.

    Upper N, Hemeida AM, Ibrahim A (2017) Moth-flame algorithm and loss sensitivity factor for optimal allocation of shunt capacitor banks in radial distribution systems. In: 2017 nineteenth international middle east power systems conference (MEPCON). IEEE, pp 851–856

  124. 124.

    Wang L, Yang R, Xu Y, Niu Q, Pardalos PM, Fei M (2013) An improved adaptive binary harmony search algorithm. Inf Sci 232:58–87

    MathSciNet  Google Scholar 

  125. 125.

    Wang M, Chen H, Yang B, Zhao X, Hu L, Cai Z, Huang H, Tong C (2017) Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267:69–84

    Google Scholar 

  126. 126.

    Wang X, Yang J, Teng X, Xia W, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recogn Lett 28(4):459–471

    Google Scholar 

  127. 127.

    Wright AH (1991) Genetic algorithms for real parameter optimization. In: Foundations of genetic algorithms, vol 1. Elsevier, pp 205–218

  128. 128.

    Yamany W, Fawzy M, Tharwat A, Hassanien AE (2015) Moth-flame optimization for training multi-layer perceptrons. In: 2015 11th international computer engineering conference (ICENCO). IEEE, pp 267–272

  129. 129.

    Yang W, Wang J, Wang R (2017a) Research and application of a novel hybrid model based on data selection and artificial intelligence algorithm for short term load forecasting. Entropy 19(2):52

    Google Scholar 

  130. 130.

    Yang X, Luo Q, Zhang J, Wu X, Zhou Y (2017b) Moth swarm algorithm for clustering analysis. In: International conference on intelligent computing. Springer, pp 503–514

  131. 131.

    Yang XS, Deb S (2009) Cuckoo search via lévy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC). IEEE, pp 210–214

  132. 132.

    Yıldız BS, Yıldız AR (2017) Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes. Mater Test 59(5):425–429

    Google Scholar 

  133. 133.

    Yousri D, AbdelAty AM, Said LA, AboBakr A, Radwan AG (2017) Biological inspired optimization algorithms for cole-impedance parameters identification. AEU-Int J Electron Commun 78:79–89

    Google Scholar 

  134. 134.

    Zawbaa HM, Emary E, Parv B, Sharawi M (2016) Feature selection approach based on moth-flame optimization algorithm. In: 2016 IEEE congress on evolutionary computation (CEC). IEEE, pp 4612–4617

  135. 135.

    Zhang H, Sun G (2002) Feature selection using tabu search method. Pattern Recogn 35(3):701–711

    MATH  Google Scholar 

  136. 136.

    Zhang J, Zhou Y, Luo Q (2018) An improved sine cosine water wave optimization algorithm for global optimization. J Intell Fuzzy Syst 34(4):2129–2141

    Google Scholar 

  137. 137.

    Zhao H, Zhao H, Guo S (2016) Using gm (1, 1) optimized by mfo with rolling mechanism to forecast the electricity consumption of inner mongolia. Appl Sci 6(1):20

    Google Scholar 

  138. 138.

    Zheng J, Lu C, Gao L (2019) Multi-objective cellular particle swarm optimization for wellbore trajectory design. Appl Soft Comput 77:106–117

    Google Scholar 

  139. 139.

    Zhou Y, Yang X, Ling Y, Zhang J (2018) Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation. Multimed Tools Appl 77(18):23699–23727

    Google Scholar 

  140. 140.

    Zingg DW, Nemec M, Pulliam TH (2008) A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization. Eur J Comput Mech/Revue Eur Méc Numér 17(1–2):103–126

    MATH  Google Scholar 

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Correspondence to Mohammad Shehab.

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Shehab, M., Abualigah, L., Al Hamad, H. et al. Moth–flame optimization algorithm: variants and applications. Neural Comput & Applic 32, 9859–9884 (2020). https://doi.org/10.1007/s00521-019-04570-6

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Keywords

  • Moth–flame optimization
  • Metaheuristic algorithms
  • Optimization problems
  • Variants of MFO