Skip to main content

Advertisement

SpringerLink
Log in

A New Set of Mutation Operators for Dragonfly Algorithm

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Dragonfly algorithm (DA) is a recently introduced, swarm intelligent algorithm and has proved its worth over real-world optimization problems. The algorithm is very efficient but is computationally expensive, has poor exploration properties, and unbalanced cohesion and alignment operation. In the present work, the concept of mutation operators has been exploited and seven new versions of DA have been proposed. Adaptive parameters, division of generations, improved exploitation phase and linearly decreasing population adaptation are also followed to improve the exploration, convergence and other properties of DA. The proposed algorithms are experimentally tested on CEC 2005 data set, CEC 2015 data set and CEC 2011 real-world benchmark problems and compared with respect to memory-based hybrid dragonfly algorithm (MHDA), hybrid memory-based dragonfly algorithm with differential evolution (DADE), sine–cosine crow search algorithm (SCCSA), success history-based adaptation differential evolution (SHADE), covariance matrix adaptation evolution strategy (CMA-ES), SHADE based on semi-parameter adaptation-based CMA-ES (LSHADE-SPACMA), hybrid particle swarm gravitational search algorithm (PSOGSA), hybrid firefly and particle swarm optimization algorithm (HFPSO) and others. Experimental and statistical results further validate that mutation clock-based DA (MDA) performs better than all other algorithms under comparison.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Salgotra, R., Singh, U.: Application of mutation operators to flower pollination algorithm. Expert Syst. Appl. 79, 112–129 (2017)

    Google Scholar 

  2. Fausto, F., Reyna-Orta, A., Cuevas, E., Andrade, Á.G., Perez-Cisneros, M.: From ants to whales: metaheuristics for all tastes. Artif. Intell. Rev. 53(1), 753–810 (2020)

    Google Scholar 

  3. Yang, X.-S.; Deb, S.: Engineering optimisation by cuckoo search. arXiv preprint arXiv:1005.2908

  4. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer (2010)

  5. Al-Betar, M.A., Awadallah, M.A.: Island bat algorithm for optimization. Expert Syst. Appl. 107, 126–145 (2018)

    Google Scholar 

  6. Dorigo, M., Birattari, M.: Ant Colony Optimization. Springer, Berlin (2010)

    Google Scholar 

  7. Kennedy, J.: Particle Swarm Optimization. Encyclopedia of Machine Learning, pp. 760–766. Springer, New York (2010)

    Google Scholar 

  8. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: International Symposium on Stochastic Algorithms, pp. 169–178. Springer (2009)

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

    Google Scholar 

  10. Yang, X.-S.: Flower pollination algorithm for global optimization. In: International Conference on Unconventional Computing and Natural Computation, pp. 240–249. Springer (2012)

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

    Google Scholar 

  12. Salgotra, R., Singh, U.: The naked mole-rat algorithm. Neural Comput. Appl. 31(12), 8837–8857 (2019)

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  14. Awadallah, M.A., Al-Betar, M.A., Bolaji, A.L., Alsukhni, E.M., Al-Zoubi, H.: Natural selection methods for artificial bee colony with new versions of onlooker bee. Soft. Comput. 23(15), 6455–6494 (2019)

    Google Scholar 

  15. Awadallah, M.A.; Al-Betar, M.A.; Bolaji, A.L.; Doush, I.A.; Hammouri, A.I.; Mafarja, M.: Island artificial bee colony for global optimization. Soft Comput, 1–27 (2020)

  16. Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)

    Google Scholar 

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

    Google Scholar 

  18. Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  20. Birbil, Şİ, Fang, S.-C.: An electromagnetism-like mechanism for global optimization. J. Global Optim. 25(3), 263–282 (2003)

    MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

  22. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    MathSciNet  MATH  Google Scholar 

  23. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)

    Google Scholar 

  24. Cuevas, E., Echavarría, A., Ramírez-Ortegón, M.A.: An optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation. Appl. Intell. 40(2), 256–272 (2014)

    Google Scholar 

  25. Tan, Y.; Zhu, Y.: Fireworks algorithm for optimization, in: International conference in swarm intelligence, Springer, pp. 355–364 (2010)

  26. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)

    Google Scholar 

  27. Atashpaz-Gargari, E.; Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE Congress on Evolutionary Computation, pp. 4661–4667. IEEE (2007)

  28. Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27(4), 1053–1073 (2016)

    MathSciNet  Google Scholar 

  29. Suresh, V., Sreejith, S.: Generation dispatch of combined solar thermal systems using dragonfly algorithm. Computing 99(1), 59–80 (2017)

    MathSciNet  MATH  Google Scholar 

  30. Suresh, M., Belwin, E.J.: Optimal dg placement for benefit maximization in distribution networks by using dragonfly algorithm. Renew.: Wind Water Solar 5(1), 4 (2018)

    Google Scholar 

  31. Khishe, M.; Safari, A.: Classification of sonar targets using an MLP neural network trained by dragonfly algorithm. Wirel. Pers. Commun., 1–20 (2019)

  32. Mafarja, M., Aljarah, I., Heidari, A.A., Faris, H., Fournier-Viger, P., Li, X., Mirjalili, S.: Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl.-Based Syst. 161, 185–204 (2018)

    Google Scholar 

  33. Daely, P.T.; Shin, S.Y.: Range based wireless node localization using dragonfly algorithm. In: 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 1012–1015. IEEE (2016)

  34. Díaz-Cortés, M.-A., Ortega-Sánchez, N., Hinojosa, S., Oliva, D., Cuevas, E., Rojas, R., Demin, A.: A multi-level thresholding method for breast thermograms analysis using dragonfly algorithm. Infrared Phys. Technol. 93, 346–361 (2018)

    Google Scholar 

  35. Hammouri, A.I., Mafarja, M., Al-Betar, M.A., Awadallah, M.A., Abu-Doush, I.: An improved dragonfly algorithm for feature selection. Knowl.-Based Syst. 203, 106131 (2020)

    Google Scholar 

  36. Shilaja, C., Ravi, K.: Optimal power flow using hybrid DA-APSO algorithm in renewable energy resources. Energy Procedia 117, 1085–1092 (2017)

    Google Scholar 

  37. Ks, S.R., Murugan, S.: Memory based hybrid dragonfly algorithm for numerical optimization problems. Expert Syst. Appl. 83, 63–78 (2017)

    Google Scholar 

  38. Song, J.; Li, S.: Elite opposition learning and exponential function steps-based dragonfly algorithm for global optimization. In: 2017 IEEE International Conference on Information and Automation (ICIA), pp. 1178–1183. IEEE (2017)

  39. Sayed, G.I., Tharwat, A., Hassanien, A.E.: Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Appl. Intell. 49(1), 188–205 (2019)

    Google Scholar 

  40. Acı, Ç.İ, Gülcan, H.: A modified dragonfly optimization algorithm for single-and multiobjective problems using Brownian motion. Comput. Intell. Neurosci. (2019). https://doi.org/10.1155/2019/6871298

    Article  Google Scholar 

  41. Xu, J., Yan, F.: Hybrid Nelder–Mead algorithm and dragonfly algorithm for function optimization and the training of a multilayer perceptron. Arab. J. Sci. Eng. 44(4), 3473–3487 (2019)

    Google Scholar 

  42. Meraihi, Y., Ramdane-Cherif, A., Acheli, D., Mahseur, M.: Dragonfly algorithm: a comprehensive review and applications. Neural Comput. Appl. 32, 16625–16646 (2020)

    Google Scholar 

  43. Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL Rep. 2005005, 2005 (2005)

    Google Scholar 

  44. Liang, J.; Qu, B.; Suganthan, P.; Chen, Q.: Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical Report201411A. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, vol. 29, pp. 625–640 (2014)

  45. Debnath, S.; Baishya, S.; Sen, D.; Arif, W.: A hybrid memory-based dragonfly algorithm with differential evolution for engineering application. Eng. Comput., 1–28 (2020)

  46. Khalilpourazari, S.; Pasandideh, S.H.R.: Sine-cosine crow search algorithm: theory and applications. Neural Comput. Appl., 1–18 (2019)

  47. Tanabe, R.; Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: 2013 IEEE Congress on Evolutionary Computation, pp. 71–78. IEEE (2013)

  48. Hansen, N., Müller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 11(1), 1–18 (2003)

    Google Scholar 

  49. Mohamed, A.W.; Hadi, A.A.; Fattouh, A.M.; Jambi, K.M.: LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 145–152. IEEE (2017)

  50. Faramarzi, A., Heidarinejad, M., Stephens, B., Mirjalili, S.: Equilibrium optimizer: a novel optimization algorithm. Knowl.-Based Syst. 191, 105190 (2020)

    Google Scholar 

  51. Mirjalili, S.; Hashim, S.Z.M.: A new hybrid PSOGSA algorithm for function optimization. In: 2010 International Conference on Computer and Information Application, pp. 374–377. IEEE (2010)

  52. Kora, P., Krishna, K.S.R.: Hybrid firefly and particle swarm optimization algorithm for the detection of bundle branch block. Int. J. Cardiovasc. Acad. 2(1), 44–48 (2016)

    Google Scholar 

  53. Aydilek, I.B.: A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl. Soft Comput. 66, 232–249 (2018)

    Google Scholar 

  54. Das, S., Suganthan, P.N.: Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems, pp. 341–359. Jadavpur University, Nanyang Technological University, Kolkata (2010)

    Google Scholar 

  55. Wilcoxon, F., Katti, S., Wilcox, R.A.: Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test. Sel. Tables Math. Stat. 1, 171–259 (1970)

    MATH  Google Scholar 

  56. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(Jan), 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  57. Sawhney, R.; Jain, R.: Modified binary dragonfly algorithm for feature selection in human papillomavirus-mediated disease treatment. In: 2018 International Conference on Communication, Computing and Internet of Things (IC3IoT), pp. 91–95. IEEE (2018)

  58. Suresh, V.; Sreejith, S.; Sudabattula, S.K.; Kamboj, V.K.: Demand response-integrated economic dispatch incorporating renewable energy sources using ameliorated dragonfly algorithm. Electr. Eng., 1–22 (2019)

  59. Yuan, Y.; Lv, L.; Wang, X.; Song, X.: Optimization of a frame structure using the coulomb force search strategy-based dragonfly algorithm. Eng. Optim., 1–17 (2019)

  60. Xu, L., Jia, H., Lang, C., Peng, X., Sun, K.: A novel method for multilevel color image segmentation based on dragonfly algorithm and differential evolution. IEEE Access 7, 19502–19538 (2019)

    Google Scholar 

  61. Shilaja, C., Arunprasath, T.: Internet of medical things-load optimization of power flow based on hybrid enhanced grey wolf optimization and dragonfly algorithm. Future Gener. Comput. Syst. 98, 319–330 (2019)

    Google Scholar 

  62. Trivedi, I.N.; Jangir, P.; Kumar, A.; Jangir, N.; Bhesdadiya, R.; Totlani, R.: A novel hybrid pso-da algorithm for global numerical optimization. In: Networking Communication and Data Knowledge Engineering, pp. 287–298. Springer (2018)

  63. Khunkitti, S., Watson, N.R., Chatthaworn, R., Premrudeepreechacharn, S., Siritaratiwat, A.: An improved DA-PSO optimization approach for unit commitment problem. Energies 12(12), 2335 (2019)

    Google Scholar 

  64. Bhesdadiya, R.; Pandya, M.H.; Trivedi, I.N.; Jangir, N.; Jangir, P.; Kumar, A.: Price penalty factors based approach for combined economic emission dispatch problem solution using dragonfly algorithm. In: 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS), pp. 436–441. IEEE (2016)

  65. Jafari, M., Chaleshtari, M.H.B.: Using dragonfly algorithm for optimization of orthotropic infinite plates with a quasi-triangular cut-out. Eur. J. Mech.-A/Solids 66, 1–14 (2017)

    MathSciNet  MATH  Google Scholar 

  66. Guha, D., Roy, P.K., Banerjee, S.: Optimal tuning of 3 degree-of-freedom proportional–integral-derivative controller for hybrid distributed power system using dragonfly algorithm. Comput. Electr. Eng. 72, 137–153 (2018)

    Google Scholar 

  67. Bashishtha, T.K.; Srivastava, L.: Nature inspired meta-heuristic dragonfly algorithms for solving optimal power flow problem. Nature

  68. Babayigit, B.: Synthesis of concentric circular antenna arrays using dragonfly algorithm. Int. J. Electron. 105(5), 784–793 (2018)

    Google Scholar 

  69. Amini, Z., Maeen, M., Jahangir, M.R.: Providing a load balancing method based on dragonfly optimization algorithm for resource allocation in cloud computing. Int. J. Netw. Distrib. Comput. 6(1), 35–42 (2017)

    Google Scholar 

  70. Vanishree, J., Ramesh, V.: Optimization of size and cost of static VAR compensator using dragonfly algorithm for voltage profile improvement in power transmission systems. Int. J. Renew. Energy Res. (IJRER) 8(1), 56–66 (2018)

    Google Scholar 

  71. Veeramsetty, V., Venkaiah, C., Kumar, D.V.: Hybrid genetic dragonfly algorithm based optimal power flow for computing LMP at DG buses for reliability improvement. Energy Syst. 9(3), 709–757 (2018)

    Google Scholar 

  72. Abhiraj, T., Aravindhababu, P.: Dragonfly optimization based reconfiguration for voltage profile enhancement in distribution systems. Int. J. Comput. Appl. 158(3), 1–4 (2017)

    Google Scholar 

  73. Aadil, F., Ahsan, W., Rehman, Z.U., Shah, P.A., Rho, S., Mehmood, I.: Clustering algorithm for internet of vehicles (IOV) based on dragonfly optimizer (CAVDO). J. Supercomput. 74(9), 4542–4567 (2018)

    Google Scholar 

  74. Khadanga, R.K., Padhy, S., Panda, S., Kumar, A.: Design and analysis of tilt integral derivative controller for frequency control in an islanded microgrid: a novel hybrid dragonfly and pattern search algorithm approach. Arab. J. Sci. Eng. 43(6), 3103–3114 (2018)

    Google Scholar 

  75. Pathania, A.K.; Mehta, S.; Rza, C.: Economic load dispatch of wind thermal integrated system using dragonfly algorithm. In: 2016 7th India International Conference on Power Electronics (IICPE), pp. 1–6. IEEE (2016)

  76. Kumar, C.A.; Vimala, R.; Britto, K.A.; Devi, S.S.: FDLA: fractional dragonfly based load balancing algorithm in cluster cloud model. Cluster Comput., 1–14 (2018)

  77. Sudabattula, S.K.; Kowsalya, M.; Velamuri, S.; Melimi, R.K.: Optimal allocation of renewable distributed generators and capacitors in distribution system using dragonfly algorithm. In: 2018 International Conference on Intelligent Circuits and Systems (ICICS), pp. 393–396. IEEE (2018)

  78. Khalilpourazari, S.; Khalilpourazary, S.: Optimization of time, cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm. Neural Comput. Appl., 1–12 (2018)

  79. Hemamalini, B.; Nagarajan, V.: Wavelet transform and pixel strength-based robust watermarking using dragonfly optimization. Multimedia Tools Appl., 1–20 (2018)

  80. Baiche, K., Meraihi, Y., Hina, M.D., Ramdane-Cherif, A., Mahseur, M.: Solving graph coloring problem using an enhanced binary dragonfly algorithm. Int. J. Swarm Intell. Res. (IJSIR) 10(3), 23–45 (2019)

    Google Scholar 

  81. Das, D.; Bhattacharya, A.; Ray, R.N.: Dragonfly algorithm for solving probabilistic economic load dispatch problems. Neural Comput. Appl., 1–17

  82. Simhadri, K.; Mohanty, B.; Rao, U.M.: Optimized 2DOF PID for AGC of multi-area power system using dragonfly algorithm. In: Applications of Artificial Intelligence Techniques in Engineering, pp. 11–22. Springer (2019)

  83. Patil, S., Kulkarni, V., Bhise, A.: Intelligent system with dragonfly optimisation for caries detection. IET Image Proc. 13(3), 429–439 (2018)

    Google Scholar 

  84. Kumar, J. K.; Ravi, G.; Sasikala, J.: Dragonfly optimization based long-term forecasting of electrical energy

  85. Reynolds, C.W.: Flocks, Herds and Schools: A Distributed Behavioral Model, vol. 21. ACM, New York (1987)

    Google Scholar 

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

    Google Scholar 

  87. Salgotra, R.; Singh, U.; Saha, S.: Improved cuckoo search with better search capabilities for solving CEC2017 benchmark problems. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–7. IEEE (2018)

  88. Salgotra, R., Singh, U., Saha, S., Gandomi, A.H.: Self adaptive cuckoo search: analysis and experimentation. Swarm Evolut. Comput. 60, 100751 (2020)

    Google Scholar 

  89. Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)

    Google Scholar 

  90. Deb, K., Deb, D.: Analysing mutation schemes for real-parameter genetic algorithms. Int. J. Artif. Intell. Soft Comput. 4(1), 1–28 (2014)

    MathSciNet  Google Scholar 

  91. Yu, C., Cai, Z., Ye, X., Wang, M., Zhao, X., Liang, G., Chen, H., Li, C.: Quantum-like mutation-induced dragonfly-inspired optimization approach. Math. Comput. Simul. 178, 259–289 (2020)

    MathSciNet  MATH  Google Scholar 

  92. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Summary the Applications of GA-Genetic Algorithm for Dealing with Some Optimal Calculations in Economics. Addison Wesley, Reading, MA (2010)

    Google Scholar 

  93. Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput. 13(3), 526–553 (2009)

    Google Scholar 

  94. Fan, H.-Y., Lampinen, J.: A trigonometric mutation operation to differential evolution. J. Global Optim. 27(1), 105–129 (2003)

    MathSciNet  MATH  Google Scholar 

  95. Ruxton, G.D.: The unequal variance t-test is an underused alternative to student’s t-test and the Mann–Whitney U test. Behav. Ecol. 17(4), 688–690 (2006)

    Google Scholar 

  96. Horner, J.B., Haken, L.: Genetic algorithms and their application to FM matching synthesis. Comput. Music J. 17(4), 17–29 (1993)

    Google Scholar 

  97. Herrera, F., Lozano, M.: Gradual distributed real-coded genetic algorithms. IEEE Trans. Evol. Comput. 4(1), 43–63 (2000)

    Google Scholar 

  98. Dukic, M.L., Dobrosavljevic, Z.S.: A method of a spread-spectrum radar polyphase code design. IEEE J. Sel. Areas Commun. 8(5), 743–749 (1990)

    Google Scholar 

  99. Mladenović, N., Petrović, J., Kovačević-Vujčić, V., Čangalović, M.: Solving spread spectrum radar polyphase code design problem by Tabu search and variable neighbourhood search. Eur. J. Oper. Res. 151(2), 389–399 (2003)

    MathSciNet  MATH  Google Scholar 

  100. Abraham, A., Das, S.: Computational Intelligence in Power Engineering, vol. 302. Springer, Berlin (2010)

    Google Scholar 

  101. Silva, I.J., Rider, M., Romero, R., Garcia, A., Murari, C.: Transmission network expansion planning with security constraints. IEE Proc.-Gener. Transm. Distrib. 152(6), 828–836 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Thapar Institute of Engineering & Technology, Patiala, India

    Rohit Salgotra, Urvinder Singh, Supreet Singh & Gurdeep Singh

  2. Department of Computer Science, Indian Institute of Technology, Patna, India

    Sriparna Saha

Authors
  1. Rohit Salgotra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Urvinder Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Supreet Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gurdeep Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sriparna Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rohit Salgotra.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest. Informed Consent All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by the any of the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salgotra, R., Singh, U., Singh, S. et al. A New Set of Mutation Operators for Dragonfly Algorithm. Arab J Sci Eng 46, 8761–8802 (2021). https://doi.org/10.1007/s13369-021-05639-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-05639-y

Keywords

Search

Navigation