Neutrosophic and Information Fusion

Journal DOI

https://doi.org/10.54216/NIF

Submit Your Paper

2836-7863ISSN (Online)

Volume 1 , Issue 1 , PP: 41-47, 2023 | Cite this article as | XML | Html | PDF | Full Length Article

Neutrosophic Sets and Metaheuristic Optimization: A Survey

Ahmed Abdelhafeez 1 * , Ahmed E Fakhry 2 , Nariman A. Khalil 3

  • 1 Faculty of Information Systems and Computer Science, October 6th University, Cairo, 12585, Egypt - (aahafeez.scis@o6u.edu.eg)
  • 2 Faculty of Information Systems and Computer Science, October 6th University, Cairo, 12585, Egypt - (ahmed.e.csis@o6u.edu.eg)
  • 3 Faculty of Information Systems and Computer Science, October 6th University, Cairo, 12585, Egypt - (narimanabdo.csis@o6u.edu.eg)
  • Doi: https://doi.org/10.54216/NIF.010105

    Received: August 15, 2022 Accepted: January 26, 2023
    Abstract

    Smarandache presents neutrosophic sets and provides a domain area that is made up of three separate subsets to reflect the various kinds of uncertainty. Neutrosophic sets are defined as the sets where every other element of the universe possesses a degree of truthiness, indeterminacy, and falsity, which range from 0 to 1, and where these degrees are subsets of the neutrosophic sets that are independent of each other. Neutrosophic sets are also known as neutrosophical subsets. In the neutrosophic sets, impreciseness is represented as truth and falsity functions, but the indeterminacy function represents degrees of belongingness and non-belongingness and differentiates between absoluteness and relativeness. Neutrosophic sets can deal with the unpredictability of the system and cut down on the paralysis brought on by conflicting information thanks to this notation. As a result, one might argue that this capacity is the single most significant benefit offered by neutrosophic sets in comparison to the many other forms of fuzzy extensions. By making use of these three functions, neutrosophic sets are able to create a domain area. This area makes it possible for various kinds of mathematical operations to be carried out separately despite the presence of uncertainty. Due to the fact that the behavior of these methodologies is inspired by Nature and its capacity for adapting to issues, in addition to the potential for combining more than one method to reach the best alternatives, metaheuristic algorithms are employed to initiate the finest or the best possible alternatives to a lot of optimization techniques. This is possible because metaheuristic algorithms have the ability to adapt to problems. The fact that numerous academics have utilized these techniques with neutrosophic science to offer several systems in recent years was the impetus for writing this overview study in the first place, which was based on the above rationale.

    Keywords :

    Intelligent Model , MCDM , Neutrosophic Sets , Metaheuristic optimization

    References

    [1]  N. E. M. Khalifa, F. Smarandache, G. Manogaran, and M. Loey, “A study of the neutrosophic set significance on deep transfer learning models: An experimental case on a limited covid-19 chest x-ray dataset,” Cognitive Computation, pp. 1–10, 2021.

    [2]  S. Broumi, A. Bakali, and A. Bahnasse, “Neutrosophic sets: An overview,” 2018.

    [3]  N. El-Hefenawy, M. A. Metwally, Z. M. Ahmed, and I. M. El-Henawy, “A review on the applications of neutrosophic sets,” Journal of Computational and Theoretical Nanoscience, vol. 13, no. 1, pp. 936–944, 2016.

    [4]  P.  Majumdar  and  S.  K.  Samanta,  “On  similarity  and  entropy  of  neutrosophic  sets,”  Journal  of Intelligent & Fuzzy Systems, vol. 26, no. 3, pp. 1245–1252, 2014.

    [5]  A.  A.  Salama  and  S.  A.  Alblowi,  “Neutrosophic  set  and  neutrosophic  topological  spaces,”  IOSR Journal of Mathematics (IOSR-JM), vol. 3, no. 4, 2012.

    [6]  B. Said  et al., “An Intelligent Traffic Control System Using Neutrosophic Sets, Rough sets, Graph Theory, Fuzzy sets and its Extended Approach: A Literature Review,” Neutrosophic Sets and Systems, vol. 50/2022: An International Journal in Information Science and Engineering, p. 10, 2022.

    [7]  A. U. Rahman, M. Saeed, F. Smarandache, and M. R. Ahmad, Development of hybrids of hypersoft set with complex fuzzy set, complex intuitionistic fuzzy set and complex neutrosophic set. Infinite Study, 2020.

    [8]  V.  Başhan,  H.  Demirel,  and  M.  Gul,  “An  FMEA-based  TOPSIS  approach  under  single  valued neutrosophic sets for maritime risk evaluation: the case of ship navigation safety,”  Soft Computing, vol. 24, no. 24, pp. 18749–18764, 2020.

    [9]  M. Abdel-Basset and M. Mohamed, “a novel and powerful framework based on neutrosophic sets to aid patients with cancer.” Elsevier, 2019.

    [10]  M. Abdel-Basset, M. Ali, and A. Atef, “Uncertainty assessments of linear time-cost tradeoffs using neutrosophic set,” Computers & Industrial Engineering, vol. 141, p. 106286, 2020.

    [11]  J. S. Chai  et al., “New similarity measures for single-valued neutrosophic sets with applications in pattern recognition and medical diagnosis problems,” Complex & Intelligent Systems, vol. 7, no. 2, pp. 703–723, 2021.

    [12]  H.  Kamacı,  H.  Garg,  and  S. Petchimuthu,  “Bipolar  trapezoidal  neutrosophic  sets  and  their  Dombi operators with applications in multicriteria decision making,”  Soft Computing, vol.  25, no. 13, pp. 8417–8440, 2021.

    [13]  M. Abdel-Basset, A. Gamal, G. Manogaran, L. H. Son, and H. V. Long, “A novel group decision making  model  based  on  neutrosophic  sets  for  heart  disease  diagnosis,”  Multimedia  Tools  and Applications, vol. 79, no. 15, pp. 9977–10002, 2020.

    [14]  S. Broumi et al., “Bipolar complex neutrosophic sets and its application in decision making problem,” Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets, pp. 677–710, 2019.

    [15]  S. A. Edalatpanah and F. Smarandache,  Data envelopment analysis for simplified neutrosophic sets. Infinite Study, 2019.

    [16]  I. Kandasamy, W. B. Vasantha, J. M. Obbineni, and F. Smarandache, “Sentiment analysis of tweets using refined neutrosophic sets,” Computers in Industry, vol. 115, p. 103180, 2020.

    [17]  T. Dokeroglu, E. Sevinc, T. Kucukyilmaz, and A. Cosar, “A survey on new generation metaheuristic algorithms,” Computers & Industrial Engineering, vol. 137, p. 106040, 2019.

    [18]  [18]  P. Agrawal, H. F. Abutarboush, T. Ganesh, and A. W. Mohamed, “Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019),”  Ieee Access, vol.  9, pp. 26766–26791, 2021.

    [19]  E. Osaba et al., “A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems,” Swarm and Evolutionary Computation, vol. 64, p. 100888, 2021.

    [20]  A.  H.  Halim,  I.  Ismail,  and  S.  Das,  “Performance  assessment  of  the  metaheuristic  optimization algorithms: an exhaustive review,” Artificial Intelligence Review, vol. 54, pp. 2323–2409, 2021.

    [21]  M. Abd Elaziz et al., “Advanced metaheuristic optimization techniques in applications of d eep neural networks: a review,” Neural Computing and Applications, pp. 1–21, 2021.

    [22]  N. Khanduja and B. Bhushan, “Recent advances and application of metaheuristic algorithms: A survey (2014–2020),” Metaheuristic and Evolutionary Computation: Algorithms and Applications, pp. 207–228, 2021.

    [23]  R. Elshaer and H. Awad, “A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants,” Computers & Industrial Engineering, vol. 140, p. 106242, 2020.

    [24]  F. Han, J. Jiang, Q.-H. Ling, and B.-Y. Su, “A survey on metaheuristic optimization for random singlehidden layer feedforward neural network,” Neurocomputing, vol. 335, pp. 261–273, 2019.

    [25]  G.  Chicco  and  A.  Mazza,  “Metaheuristic  optimization  of  power  and  energy  systems:  underlying principles and main issues of the ‘rush to heuristics,’”  energies, vol. 13, no. 19, p. 5097, 2020.

    [26]  P. Wang, Y. Zhou, Q. Luo, C. Han, Y. Niu, and M. Lei, “Complex-valued encoding metaheuristic optimization algorithm: A comprehensive survey,” Neurocomputing, vol. 407, pp. 313–342, 2020.

    [27]  E.-G.  Talbi,  “Machine  learning  into  metaheuristics:  A  survey  and  taxonomy,”  ACM  Computing Surveys (CSUR), vol. 54, no. 6, pp. 1–32, 2021.

    [28]  C. Huang, Y. Li, and X. Yao, “A survey of automatic parameter tuning methods for metaheuristics,” IEEE transactions on evolutionary computation, vol. 24, no. 2, pp. 201–216, 2019.

    [29]  T.  W.  Liao  and  G.  Li,  “Metaheuristic-based  inverse  design  of  materials–a  survey,”  Journal  of Materiomics, vol. 6, no. 2, pp. 414–430, 2020.

    [30]  M. Papadimitrakis, N. Giamarelos, M. Stogiannos, E. N. Zois, N.-I. Livanos, and A. Alexandridis, “Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power f low optimization applications,” Renewable and Sustainable Energy Reviews, vol. 145, p. 111072, 2021.

    [31]  W. K. Wong and C. I. Ming, “A review on metaheuristic algorithms: recent trends, benchmarking and applications,”  in  2019  7th  International  Conference  on  Smart  Computing  &  Communications (ICSCC), 2019, pp. 1–5.

    [32]  M.  M.  Nasef,  F.  T.  Eid,  and  A.  M.  Sauber,  “Skeletal  scintigraphy  image  enhancement  based neutrosophic sets and salp swarm algorithm,” Artificial Intelligence in Medicine, vol. 109, p. 101953, 2020.

    [33]  T. S. C. A. Palanisamy, M. Jayaraman, K. Vellingiri, and Y. Guo, “Optimization-based neutrosophic set for medical image processing,” in Neutrosophic set in medical image analysis, Elsevier, 2019, pp. 189–206.

    [34]  A.  S.  Ashour  and  Y.  Guo,  “Advanced  optimization-based  neutrosophic  sets  for  medical  image denoising,” in Neutrosophic set in medical image analysis, Elsevier, 2019, pp. 101–121.

    [35]  A. M. Anter and A. E. Hassenian, “Computational intelligence optimization approach based on particle swarm  optimizer  and  neutrosophic  set  for  abdominal  CT  liver  tumor  segmentation,”  Journal  of Computational Science, vol. 25, pp. 376–387, 2018.

    [36]  S. H. Basha, A. M. Anter, A. E. Hassanien, and A. Abdalla, “Hybrid intelligent model for classifying chest  X-ray  images  of  COVID-19  patients  using  genetic  algorithm  and  neutrosophic  logic,”  Soft Computing, pp. 1–16, 2021.

    [37]  G.  I.  Sayed  and  A.  E.  Hassanien,  “Moth-flame  swarm  optimization  with  neutrosophic  sets  for automatic mitosis detection in breast cancer histology images,” Applied Intelligence, vol. 47, no. 2, pp. 397–408, 2017.

    Cite This Article As :
    Abdelhafeez, Ahmed. , E, Ahmed. , A., Nariman. Neutrosophic Sets and Metaheuristic Optimization: A Survey. Neutrosophic and Information Fusion, vol. , no. , 2023, pp. 41-47. DOI: https://doi.org/10.54216/NIF.010105
    Abdelhafeez, A. E, A. A., N. (2023). Neutrosophic Sets and Metaheuristic Optimization: A Survey. Neutrosophic and Information Fusion, (), 41-47. DOI: https://doi.org/10.54216/NIF.010105
    Abdelhafeez, Ahmed. E, Ahmed. A., Nariman. Neutrosophic Sets and Metaheuristic Optimization: A Survey. Neutrosophic and Information Fusion , no. (2023): 41-47. DOI: https://doi.org/10.54216/NIF.010105
    Abdelhafeez, A. , E, A. , A., N. (2023) . Neutrosophic Sets and Metaheuristic Optimization: A Survey. Neutrosophic and Information Fusion , () , 41-47 . DOI: https://doi.org/10.54216/NIF.010105
    Abdelhafeez A. , E A. , A. N. [2023]. Neutrosophic Sets and Metaheuristic Optimization: A Survey. Neutrosophic and Information Fusion. (): 41-47. DOI: https://doi.org/10.54216/NIF.010105
    Abdelhafeez, A. E, A. A., N. "Neutrosophic Sets and Metaheuristic Optimization: A Survey," Neutrosophic and Information Fusion, vol. , no. , pp. 41-47, 2023. DOI: https://doi.org/10.54216/NIF.010105