Advertisement

Salp swarm algorithm: a comprehensive survey

  • Laith AbualigahEmail author
  • Mohammad Shehab
  • Mohammad Alshinwan
  • Hamzeh Alabool
Review Article
  • 17 Downloads

Abstract

This paper completely introduces an exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields, such as machine learning, engineering design, wireless networking, image processing, and power energy. This review shows the available literature on SSA, including its variants, like binary, modifications and multi-objective. Followed by its applications, assessment and evaluation, and finally the conclusions, which focus on the current works on SSA, suggest possible future research directions.

Keywords

Salp swarm algorithm Meta-heuristic optimization algorithms Optimization problems Bio-inspired algorithms 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

References

  1. 1.
    Shehab M, Khader AT, Al-Betar MA (2017) A survey on applications and variants of the cuckoo search algorithm. Appl Soft Comput 61:1041–1059CrossRefGoogle Scholar
  2. 2.
    Hazir E, Erdinler ES, Koc KH (2018) Optimization of CNC cutting parameters using design of experiment (DOE) and desirability function. J For Res 29:1423–1434CrossRefGoogle Scholar
  3. 3.
    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 73:4773–4795CrossRefGoogle Scholar
  4. 4.
    Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5:19Google Scholar
  5. 5.
    Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved Krill herd algorithm. Appl Intell 48:4047–4071CrossRefGoogle Scholar
  6. 6.
    Abualigah LM, Khader AT, Hanandeh ES (2018) A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125CrossRefGoogle Scholar
  7. 7.
    Kirkpatrick S (1984) Optimization by simulated annealing: quantitative studies. J Stat Phys 34:975–986MathSciNetCrossRefGoogle Scholar
  8. 8.
    Abualigah LM, Khader AT, Hanandeh ES (2018) A novel weighting scheme applied to improve the text document clustering techniques. In: Zelinka I, Vasant P, Duy VH, Dao TT (eds) Innovative computing, optimization and its applications. Springer, Berlin, pp 305–320CrossRefGoogle Scholar
  9. 9.
    Glover F (1989) Tabu search—part I. ORSA J Comput 1:190–206zbMATHCrossRefGoogle Scholar
  10. 10.
    Abualigah LM, Sawaie AM, Khader AT, Rashaideh H, Al-Betar MA, Shehab M (2017) \(\beta\)-Hill climbing technique for the text document clustering. New Trends in Information Technology (NTIT)-2017 60Google Scholar
  11. 11.
    Koza JR, Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT Press, BerlinzbMATHGoogle Scholar
  12. 12.
    Rajabioun R (2011) Cuckoo optimization algorithm. Appl. Soft Comput 11:5508–5518CrossRefGoogle Scholar
  13. 13.
    Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimisation. Preprint arXiv:1003.1409
  14. 14.
    Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 2. IEEE, New York, pp 1470–1477Google Scholar
  15. 15.
    Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68CrossRefGoogle Scholar
  16. 16.
    Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Pelta DA, Krasnogor N, Dumitrescu D, Chira C, Lung R (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, pp 65–74CrossRefGoogle Scholar
  17. 17.
    Bolaji AL, Al-Betar MA, Awadallah MA, Khader AT, Abualigah LM (2016) A comprehensive review: Krill herd algorithm (KH) and its applications. Appl Soft Comput 49:437–446CrossRefGoogle Scholar
  18. 18.
    Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112CrossRefGoogle Scholar
  19. 19.
    Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073CrossRefGoogle Scholar
  20. 20.
    Yang X-S, Deb S (2009) Cuckoo search via lévy flights. In: 2009 World congress on nature and biologically inspired computing (NaBIC). IEEE, New York, pp 210–214Google Scholar
  21. 21.
    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, ComputerGoogle Scholar
  22. 22.
    Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249CrossRefGoogle Scholar
  23. 23.
    Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249CrossRefGoogle Scholar
  24. 24.
    Niu B, Wang H (2012) Bacterial colony optimization. Discrete Dyn Nat Soc 2012:28MathSciNetzbMATHGoogle Scholar
  25. 25.
    Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12:702–713CrossRefGoogle Scholar
  26. 26.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  27. 27.
    Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98CrossRefGoogle Scholar
  28. 28.
    Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the 6th international symposium on micro machine and human science. IEEE, New York, pp 39–43Google Scholar
  29. 29.
    Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70CrossRefGoogle Scholar
  30. 30.
    Henschke N, Everett JD, Doblin MA, Pitt KA, Richardson AJ, Suthers IM (2014) Demography and interannual variability of salp swarms (thalia democratica). Mar Biol 161:149–163CrossRefGoogle Scholar
  31. 31.
    McCauley DJ, Pinsky ML, Palumbi SR, Estes JA, Joyce FH, Warner RR (2015) Marine defaunation: animal loss in the global ocean. Science 347:1255641CrossRefGoogle Scholar
  32. 32.
    Shehab M, Khader AT, Al-Betar M (2016) New selection schemes for particle swarm optimization. IEEJ Trans Electron Inf Syst 136:1706–1711Google Scholar
  33. 33.
    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 114:163–191CrossRefGoogle Scholar
  34. 34.
    Zhang J, Wang Z, Luo X (2018) Parameter estimation for soil water retention curve using the salp swarm algorithm. Water 10:815CrossRefGoogle Scholar
  35. 35.
    Shehab M, Khader AT, Laouchedi M (2017) Modified cuckoo search algorithm for solving global optimization problems. In: International conference of reliable information and communication technology. Springer, Berlin, pp 561–570Google Scholar
  36. 36.
    Shehab M, Khader AT, Laouchedi M, Alomari OA (2018) Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization. J Supercomput 75:1–28Google Scholar
  37. 37.
    Shehab M, Khader AT, Laouchedi M (2018) A hybrid method based on cuckoo search algorithm for global optimization problems. J ICT 17:469–491Google Scholar
  38. 38.
    Ibrahim RA, Ewees AA, Oliva D, Elaziz MA, Lu S (2018) Improved salp swarm algorithm based on particle swarm optimization for feature selection. J Ambient Intell Humaniz Comput 10:1–15Google Scholar
  39. 39.
    Abusnaina AA, Ahmad S, Jarrar R, Mafarja M (2018) Training neural networks using salp swarm algorithm for pattern classification. In: Proceedings of the 2nd international conference on future networks and distributed systems. ACM, New York, p 17Google Scholar
  40. 40.
    Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466CrossRefGoogle Scholar
  41. 41.
    Rizk-Allah RM, Hassanien AE, Elhoseny M, Gunasekaran M (2018) A new binary salp swarm algorithm: development and application for optimization tasks. Neural Comput Appl 31:1–23Google Scholar
  42. 42.
    Faris H, Mafarja MM, Heidari AA, Aljarah I, Ala’M A-Z, Mirjalili S, Fujita H (2018) An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl Based Syst 154:43–67CrossRefGoogle Scholar
  43. 43.
    Aljarah I, Mafarja M, Heidari AA, Faris H, Zhang Y, Mirjalili S (2018) Asynchronous accelerating multi-leader salp chains for feature selection. Appl Soft Comput 71:964–979CrossRefGoogle Scholar
  44. 44.
    Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for Krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435CrossRefGoogle Scholar
  45. 45.
    Wang D, Zhou Y, Jiang S, Liu X (2018) A simplex method-based salp swarm algorithm for numerical and engineering optimization. In: International conference on intelligent information processing. Springer, Berlin, pp 150–159Google Scholar
  46. 46.
    Hegazy AE, Makhlouf M, El-Tawel GS (2018) Improved salp swarm algorithm for feature selection. J King Saud Univ Comput Inf Sci.  https://doi.org/10.1016/j.jksuci.2018.06.003 CrossRefGoogle Scholar
  47. 47.
    Sahu PC, Mishra S, Prusty RC, Panda S (2018) Improved-salp swarm optimized type-II fuzzy controller in load frequency control of multi area islanded AC microgrid. Sustain Energy Grids Netw 16:380–392CrossRefGoogle Scholar
  48. 48.
    Yang B, Zhong L, Zhang X, Shu H, Yu T, Li H, Jiang L, Sun L (2019) Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition. J Clean Product 215:1203–1222CrossRefGoogle Scholar
  49. 49.
    Sun Z-X, Hu R, Qian B, Liu B, Che G-L (2018) Salp swarm algorithm based on blocks on critical path for reentrant job shop scheduling problems. In: International conference on intelligent computing. Springer, Berlin, pp 638–648CrossRefGoogle Scholar
  50. 50.
    Patnana N, Pattnaik S, Singh V (2018) Salp swarm optimization based PID controller tuning for doha reverse osmosis desalination plant. Int J Pure Appl Math 119:12707–12720Google Scholar
  51. 51.
    Baygi SMH, Karsaz A, Elahi A (2018) A hybrid optimal PID-fuzzy control design for seismic exited structural system against earthquake: a salp swarm algorithm. In: 2018 6th Iranian joint congress on fuzzy and intelligent systems (CFIS). IEEE, New York, pp 220–225Google Scholar
  52. 52.
    Baygi SMH, Karsaz A (2018) A hybrid optimal PID-LQR control of structural system: a case study of salp swarm optimization. In: 2018 3rd conference on swarm intelligence and evolutionary computation (CSIEC). IEEE, New York, pp 1–6Google Scholar
  53. 53.
    Singh N, Chiclana F, Magnot J-P et al (2019) A new fusion of salp swarm with sine cosine for optimization of non-linear functions. Eng Comput.  https://doi.org/10.1007/s00366-018-00696-8 CrossRefGoogle Scholar
  54. 54.
    Wang J, Gao Y, Chen X (2018) A novel hybrid interval prediction approach based on modified lower upper bound estimation in combination with multi-objective salp swarm algorithm for short-term load forecasting. Energies 11:1561CrossRefGoogle Scholar
  55. 55.
    Khamees M, Albakry A, Shaker K (2018) Multi-objective feature selection: hybrid of salp swarm and simulated annealing approach. In: International conference on new trends in information and communications technology applications. Springer, Berlin, pp 129–142Google Scholar
  56. 56.
    Shehab M, Khader AT, Al-Betar MA, Abualigah LM (2017) Hybridizing cuckoo search algorithm with hill climbing for numerical optimization problems. In: 2017 8th international conference on information technology (ICIT). IEEE, New York, pp 36–43Google Scholar
  57. 57.
    Asaithambi S, Rajappa M (2018) Swarm intelligence-based approach for optimal design of cmos differential amplifier and comparator circuit using a hybrid salp swarm algorithm. Rev Sci Instrum 89:054702CrossRefGoogle Scholar
  58. 58.
    Sayed GI, Khoriba G, Haggag MH (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell 48:3462–3481CrossRefGoogle Scholar
  59. 59.
    Ahmed S, Mafarja M, Faris H, Aljarah I (2018) Feature selection using salp swarm algorithm with chaos. In: Proceedings of the 2nd international conference on intelligent systems, metaheuristics and swarm intelligence. ACM, New York, pp 65–69Google Scholar
  60. 60.
    Meraihi Y, Ramdane-Cherif A, Mahseur M, Achelia D (2018) A chaotic binary salp swarm algorithm for solving the graph coloring problem. In: International symposium on modelling and implementation of complex systems. Springer, Berlin, pp 106–118Google Scholar
  61. 61.
    Ateya AA, Muthanna A, Vybornova A, Algarni AD, Abuarqoub A, Koucheryavy Y, Koucheryavy A (2019) Chaotic salp swarm algorithm for sdn multi-controller networks. Eng Sci Technol Int J.  https://doi.org/10.1016/j.jestch.2018.12.015 CrossRefGoogle Scholar
  62. 62.
    Hegazy AE, Makhlouf M, El-Tawel GS (2018) Feature selection using chaotic salp swarm algorithm for data classification. Arab J Sci Eng 44:1–16Google Scholar
  63. 63.
    Song M, Chen D (2018) An improved knowledge-informed NSGA-II for multi-objective land allocation (MOLA). Geospat Inf Sci 21:273–287CrossRefGoogle Scholar
  64. 64.
    Abualigah LM, Khader AT, Al-Betar MA (2016) Multi-objectives-based text clustering technique using K-mean algorithm. In: 2016 7th international conference on computer science and information technology (CSIT). IEEE, New York, pp 1–6Google Scholar
  65. 65.
    Tolba M, Rezk H, Diab A, Al-Dhaifallah M (2018) A novel robust methodology based salp swarm algorithm for allocation and capacity of renewable distributed generators on distribution grids. Energies 11:2556CrossRefGoogle Scholar
  66. 66.
    Jiang P, Li R, Li H (2019) Multi-objective algorithm for the design of prediction intervals for wind power forecasting model. Appl Math Model 67:101–122MathSciNetCrossRefGoogle Scholar
  67. 67.
    Benmiloud O, Arif S (2018) Optimal dynamic equivalence based on multi-objective formulation. In: 2018 international conference on electrical sciences and technologies in maghreb (CISTEM). IEEE, New York, pp 1–6Google Scholar
  68. 68.
    Yousri D, AbdelAty AM, Said LA, Elwakil A, Maundy B, Radwan AG (2019) Parameter identification of fractional-order chaotic systems using different meta-heuristic optimization algorithms. Nonlinear Dyn 95:1–52CrossRefGoogle Scholar
  69. 69.
    Hao Y, Tian C (2019) The study and application of a novel hybrid system for air quality early-warning. Appl Soft Comput 74:729–746MathSciNetCrossRefGoogle Scholar
  70. 70.
    Abbassi R, Abbassi A, Heidari AA, Mirjalili S (2019) An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Manag 179:362–372CrossRefGoogle Scholar
  71. 71.
    Baliarsingh SK, Vipsita S, Muhammad K, Dash B, Bakshi S (2019) Analysis of high-dimensional genomic data employing a novel bio-inspired algorithm. Appl Soft Comput 77:520–532CrossRefGoogle Scholar
  72. 72.
    El-Fergany AA (2018) Extracting optimal parameters of pem fuel cells using salp swarm optimizer. Renew Energy 119:641–648CrossRefGoogle Scholar
  73. 73.
    Ekinci S, Hekimoglu B (2018) Parameter optimization of power system stabilizer via salp swarm algorithm. In: 2018 5th international conference on electrical and electronic engineering (ICEEE). IEEE, New York, pp 143–147Google Scholar
  74. 74.
    Papadopoulos TA, Ceylan O, Papagiannis GK (2018) Two-layer earth structure parameter estimation and seasonal analysis. In: 2018 53rd international universities power engineering conference (UPEC). IEEE, New York, pp 1–6Google Scholar
  75. 75.
    Abualigah LMQ (2019) Feature selection and enhanced Krill herd algorithm for text document clustering. Springer, BerlinCrossRefGoogle Scholar
  76. 76.
    Ibrahim HT, Mazher WJ, Ucan ON, Bayat O (2017) Feature selection using salp swarm algorithm for real biomedical datasets. Int J Comput Sci Netw Secur 12:13CrossRefGoogle Scholar
  77. 77.
    Zhang J, Teng Y-F, Chen W (2018) Support vector regression with modified firefly algorithm for stock price forecasting. Appl Intell 49:1–17Google Scholar
  78. 78.
    Hussien AG, Hassanien AE, Houssein EH (2017) Swarming behaviour of salps algorithm for predicting chemical compound activities. In: 2017 8th international conference on intelligent computing and information systems (ICICIS). IEEE, New York, pp 315–320Google Scholar
  79. 79.
    Zhang X, Wang J, Liu Z, Wang J (2019) Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance. ISA Trans 84:283–295CrossRefGoogle Scholar
  80. 80.
    Esfe MH, Ahangar MRH, Rejvani M, Toghraie D, Hajmohammad MH (2016) Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO\(_{2}\) using experimental data. Int Commun Heat Mass Transf 75:192–196CrossRefGoogle Scholar
  81. 81.
    Bairathi D, Gopalani D (2019) Salp swarm algorithm (SSA) for training feed-forward neural networks. In: Bansal JC, Das KN, Nagar A, Deep K, Ojha AK (eds) Soft computing for problem solving. Springer, Berlin, pp 521–534CrossRefGoogle Scholar
  82. 82.
    Kouba NEY, Boudour M (2019) A brief review and comparative study of nature-inspired optimization algorithms applied to power system control. In: Li X, Wong KC (eds) Natural computing for unsupervised learning. Springer, Berlin, pp 35–49CrossRefGoogle Scholar
  83. 83.
    Mohapatra TK, Sahu BK (2018) Design and implementation of SSA based fractional order PID controller for automatic generation control of a multi-area, multi-source interconnected power system. In: 2018 Technologies for smart-city energy security and power (ICSESP). IEEE, New York, pp 1–6Google Scholar
  84. 84.
    Sahu PC, Prusty RC, Panda S (2018) Salp swarm optimized multistage PDF plus \((1+ {\text{PI}})\) controller in agc of multi source based nonlinear power system. In: International conference on soft computing systems. Springer, Berlin, pp 789–800CrossRefGoogle Scholar
  85. 85.
    El-Fergany AA, Hasanien HM (2019) Salp swarm optimizer to solve optimal power flow comprising voltage stability analysis. Neural Comput Appl.  https://doi.org/10.1007/s00521-019-04029-8 CrossRefGoogle Scholar
  86. 86.
    Guha D, Roy P, Banerjee S (2018) A maiden application of salp swarm algorithm optimized cascade tilt-integral-derivative controller for load frequency control of power systems. IET Gener Transm Distrib 9:25–36Google Scholar
  87. 87.
    Kuyu YC, Vatansever F (2018) Real loss minimization in power systems via recent optimization techniques. In: 2018 2nd international symposium on multidisciplinary studies and innovative technologies (ISMSIT). IEEE, New York, pp 1–4Google Scholar
  88. 88.
    Rezk H, Fathy A (2017) A novel optimal parameters identification of triple-junction solar cell based on a recently meta-heuristic water cycle algorithm. Sol Energy 157:778–791CrossRefGoogle Scholar
  89. 89.
    Mohamed MA, Diab AAZ, Rezk H (2019) Partial shading mitigation of pv systems via different meta-heuristic techniques. Renew Energy 130:1159–1175CrossRefGoogle Scholar
  90. 90.
    Barik AK, Das DC (2018) Active power management of isolated renewable microgrid generating power from rooftop solar arrays, sewage waters and solid urban wastes of a smart city using salp swarm algorithm. In: 2018 technologies for smart-city energy security and power (ICSESP). IEEE, New York, pp 1–6Google Scholar
  91. 91.
    Chang Z, Cao J, Zhang Y (2018) A novel image segmentation approach for wood plate surface defect classification through convex optimization. J For Res 29:1789–1795CrossRefGoogle Scholar
  92. 92.
    Dhal KG, Ray S, Das A, Das S (2018) A survey on nature-inspired optimization algorithms and their application in image enhancement domain. Arch Comput Methods Eng 26:1–32MathSciNetGoogle Scholar
  93. 93.
    Ibrahim A, Ahmed A, Hussein S, Hassanien AE (2018) Fish image segmentation using salp swarm algorithm. In: International conference on advanced machine learning technologies and applications. Springer, Berlin, pp 42–51CrossRefGoogle Scholar
  94. 94.
    Liu X, Xu H (2018) Application on target localization based on salp swarm algorithm. In: 2018 37th Chinese control conference (CCC). IEEE, New York, pp 4542–4545Google Scholar
  95. 95.
    Khalid A, Javaid N, Mateen A, Ilahi M, Saba T, Rehman A (2019) Enhanced time-of-use electricity price rate using game theory. Electronics 8:48CrossRefGoogle Scholar
  96. 96.
    Khalid A, Khan ZA, Javaid N (2018) Game theory based electric price tariff and salp swarm algorithm for demand side management. In: 2018 5th HCT information technology trends (ITT). IEEE, New York, pp 99–103Google Scholar
  97. 97.
    Erdoğmuş P (2018) Nature inspired optimization algorithms and their performance on the solution of nonlinear equation systems. Sakarya Univ J Comput Inf Sci 1:44–57Google Scholar
  98. 98.
    Asasi MS, Ahanch M, Holari YT (2018) Optimal allocation of distributed generations and shunt capacitors using salp swarm algorithm. In: Iranian conference on electrical engineering (ICEE). IEEE, New York, pp 1166–1172Google Scholar
  99. 99.
    Sereshki AB, Derakhshani A (2018) Optimizing the mechanical stabilization of earth walls with metal strips: applications of swarm algorithms. Arab J Sci Eng 44:1–14Google Scholar
  100. 100.
    Qais MH, Hasanien HM, Alghuwainem S (2019) Enhanced salp swarm algorithm: application to variable speed wind generators. Eng Appl Artif Intell 80:82–96CrossRefGoogle Scholar
  101. 101.
    Moth–flame optimization algorithm: variants and applicationsGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Computer Sciences and InformaticsAmman Arab UniversityAmmanJordan
  2. 2.Department of Computer ScienceAqaba University of TechnologyAqabaJordan
  3. 3.College of Computing and InformaticsSaudi Electronic UniversityAbhaSaudi Arabia

Personalised recommendations