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A hybrid memory-based dragonfly algorithm with differential evolution for engineering application

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Abstract

The dragonfly algorithm (DA) is a swarm-based stochastic algorithm which possesses static and dynamic behavior of swarm and is gaining meaningful popularity due to its low computational cost and fast convergence in solving complex optimization problems. However, it lacks internal memory and is thereby not able to keep track of its best solutions in previous generations. Furthermore, the solution also lacks in diversity and thereby has a propensity of getting trapped in the local optimal solution. In this paper, an iterative-level hybridization of dragonfly algorithm (DA) with differential evolution (DE) is proposed and named as hybrid memory-based dragonfly algorithm with differential evolution (DADE). The reason behind selecting DE is for its computational ability, fast convergence and capability in exploring the solution space through the use of crossover and mutation techniques. Unlike DA, in DADE the best solution in a particular iteration is stored in memory and proceeded with DE which enhances population diversity with improved mutation and accordingly increases the probability of reaching global optima efficiently. The efficiency of the proposed algorithm is measured based on its response to standard set of 74 benchmark functions including 23 standard mathematical benchmark functions, 6 composite benchmark function of CEC2005, 15 benchmark functions of CEC2015 and 30 benchmark function of CEC2017. The DADE algorithm is applied to engineering design problems such as welded beam deign, pressure vessel design, and tension/compression spring design. The algorithm is also applied to the emerging problem of secondary user throughput maximization in an energy-harvesting cognitive radio network. A comparative performance analysis between DADE and other most popular state-of-the-art optimization algorithms is carried out and significance of the results is deliberated. The result demonstrates significant improvement and prominent advantages of DADE compared to conventional DE, PSO and DA in terms of various performance measuring parameters. The results of the DADE algorithm applied on some important engineering design problems are encouraging and validate its appropriateness in the context of solving interesting practical engineering challenges. Lastly, the statistical analysis of the algorithm is also performed and is compared with other powerful optimization algorithms to establish its superiority.

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References

  1. Dorigo M, Thomas S (2004) Ant colony optimization. MIT Press eBooks, Cambridge

    Book  Google Scholar 

  2. Blum C, Li X (2008) Swarm intelligence in optimization. In: Blum C, Merkle D (eds) Swarm intelligence: Introduction and applications. Springer Berlin Heidelberg, Berlin, pp 43–85

    Chapter  Google Scholar 

  3. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Perth 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968

    Article  Google Scholar 

  4. Omran M, Engelbrecht AP, Salman A (2005) Particle swarm optimization methods for image clustering. Int J Pattern Recognit Artif Intell 19(3):297–321. https://doi.org/10.1142/S0218001405004083

    Article  Google Scholar 

  5. AlRashidi MR, El-Hawary ME (2008) A survey of particle swarm optimization applications in electric power systems. IEEE Trans Evol Comput 13(4):913–918. https://doi.org/10.1109/TEVC.2006.880326

    Article  Google Scholar 

  6. He S, Prempain E, Wu QH (2007) An improved particle swarm optimizer for mechanical design optimization problems. Eng Optim 36(5):585–605. https://doi.org/10.1080/03052150410001704854

    Article  MathSciNet  Google Scholar 

  7. Nimtawat A, Nanakorn P (2011) Simple particle swarm optimization for solving beam-slab layout design problems. Proc Eng 14:1392–1398. https://doi.org/10.1016/j.proeng.2011.07.175

    Article  Google Scholar 

  8. Dorigo M (2007) Ant colony optimization, IRIDAI. Schol Pedia 2(3):1461. https://doi.org/10.4249/scholarpedia.1461

    Article  MathSciNet  Google Scholar 

  9. Akay B, Karaboga D (2012) Artificial bee colonial algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014. https://doi.org/10.1007/s10845-010-0393-4

    Article  Google Scholar 

  10. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Rasnogor N (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Springer Berlin Heidelberg, Berlin, pp 65–74

    Chapter  Google Scholar 

  11. Yang XS (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds), Stochastic algorithms: foundations and applications: 5th international symposium, SAGA 2009, Sapporo, Japan, pp. 169–178. Berlin, Heidelberg: Springer Berlin Heidelberg.https://doi.org/10.1007/978-3-642-04944-6_14.

  12. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  13. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  14. Sharma A, Sharma A, Panigrahi B, Kiran D, Kumar R (2016) Ageist spider monkey optimization algorithm. Swarm Evol Comput 28:58–77. https://doi.org/10.1016/j.swevo.2016.01.002

    Article  Google Scholar 

  15. Wang GG (2016) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet Comput. https://doi.org/10.1007/s12293-016-0212-3

    Article  Google Scholar 

  16. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  17. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010

    Article  Google Scholar 

  18. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, faris H, Mirjalili SM, (2017) salp swarm algorithm. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002

    Article  Google Scholar 

  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(4):1053–1073. https://doi.org/10.1007/s00521-015-1920-1

    Article  MathSciNet  Google Scholar 

  20. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper Optimisation Algorithm: Theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004

    Article  Google Scholar 

  21. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput & Applic 27:495–513. https://doi.org/10.1007/s00521-015-1870-7

    Article  Google Scholar 

  22. Mirjalili S (2016) SCA: A sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  23. Thangaraj R, Pant M, Abraham A, Bouvry P (2011) Particle swarm optimization: Hybridization perspectives and experimental illustrations. Appl Math Comput 217(12):5208–5226. https://doi.org/10.1016/j.amc.2010.12.053

    Article  MATH  Google Scholar 

  24. Chen WN, Lin Y, Chen N, Zhan ZH, Chung HSH, Li Y, Shi YH (2013) Particle swarm optimization with an aging leader and challengers. IEEE Trans Evol Comput 17(2):241–258. https://doi.org/10.1109/TEVC.2011.2173577

    Article  Google Scholar 

  25. Zhang WJ, Xie XF (2003) DEPSO: hybrid particle swarm with differential evolution operator. IEEE Int Conf Syst Man and Cybern. https://doi.org/10.1109/ICSMC.2003.1244483

    Article  Google Scholar 

  26. Storn R, Price K (1997) Differential evolution—a simple and efficient Heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359. https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  27. Liu H, Xu G, Ding GY, Sun YB (2014) Human behavior based particle swarm optimization, the scientific world journal. Hindawi Publishing Corporation, London

    Google Scholar 

  28. Şenel FA, Gökçe F, Yüksel AS, Yiğit T (2018) A novel hybrid PSO–GWO algorithm for optimization problems. Eng Comput. https://doi.org/10.1007/s00366-018-0668-5

    Article  Google Scholar 

  29. Singh N, Chiclana F, Magnot JP (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

    Article  Google Scholar 

  30. Elsayed SM, Sarker RA, Essam DL (2012) An improved self-adaptive differential evolution algorithm for optimization problems. IEEE Trans Ind Inf 9(1):89–99. https://doi.org/10.1109/TII.2012.2198658

    Article  Google Scholar 

  31. Laizhong C, Genghui L, Zexuan Z, Zhong M, Zhenkun W, Nan L (2019) Differential evolution algorithm with dichotomy-based parameter space compression. Soft Comput 23:3643. https://doi.org/10.1007/s00500-018-3015-2

    Article  Google Scholar 

  32. Bin X, Lili T, Xu C, Wushan C (2019) Adaptive differential evolution with multi-population-based mutation operators for constrained optimizatio. Soft Comput 23: 3423. https://doi.org/10.1007/s00500-017-3001-0

  33. Pavai G, Geetha TV (2019) New crossover operators using dominance and co-dominance principles for faster convergence of genetic algorithms. Soft Comput 23: 3661. https://doi.org/10.1007/s00500-018-3016-1.

  34. Yang XS (2014) Chapter 2—analysis of algorithms. In: Yang XS (ed) Nature-inspired optimization algorithms. Elsevier, Oxford, pp 23–44

    Chapter  Google Scholar 

  35. Sree Ranjini KS, Murugan S (2017) Memory based hybrid Dragonfly algorithm for numerical optimization problem. Expart Syst Appl 83:63–78. https://doi.org/10.1016/j.eswa.2017.04.033

    Article  Google Scholar 

  36. Liu H, Abraham A, Zhang W (2012) A fuzzy adaptive turbulent particle swarm optimization. Int J Innov Comput Appl 1(1):39–47. https://doi.org/10.1504/IJICA.2007.013400

    Article  Google Scholar 

  37. Chen YP, Peng WC, Jian MC (2007) Particle swarm optimization with recombination and dynamic linkage discovery. IEEE Trans Syst Man Cybern B 37(6):1460–1470. https://doi.org/10.1109/TSMCB.2007.904019

    Article  Google Scholar 

  38. Andrews PS (2006) An investigation into mutation operators for particle swarm optimization. In: Proc. IEEE Congress on Evolutionary Computation (CEC), pp. 1044–1051. https://doi.org/10.1109/CEC.2006.1688424.

  39. Angeline PJ (1998) Using selection to improve particle swarm optimization. In: Proc. IEEE Congress on Evolutionary Computation (CEC), pp. 84–89, https://doi.org/10.1109/ICEC.1998.699327

  40. Rao SS (2009) Chapter 8, engineering optimization—theory and practice, 4th edn. Wiley, Hoboken

    Book  Google Scholar 

  41. Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison Wesley Publishing Company, Boston

    MATH  Google Scholar 

  42. Deb K (1999) An introduction to genetic algorithms. Sadhana 24:293–315. https://doi.org/10.1007/BF02823145

    Article  MathSciNet  MATH  Google Scholar 

  43. Kubota N, Fukuda T (1997) Genetic algorithms with age structure. Soft Comput 1(4):155–161. https://doi.org/10.1007/s005000050017

    Article  Google Scholar 

  44. Ghosh A, Tsutsui S, Tanaka H (1996) Individual aging in genetic algorithms,” in Proc. Conference on Intelligent Information Systems, Australian New Zealand, pp. 276–279. https://doi.org/10.1109/ANZIIS.1996.573957

  45. Goldsmith TC (2004) Aging as an evolved characteristic Weismann’s theory reconsidered. Med Hypotheses Version 62(2):304–308. https://doi.org/10.1016/S0306-9877(03)00337-2

    Article  Google Scholar 

  46. Goldsmith TC (2006) The evolution of aging. Azinet Press, Crownsville

    Google Scholar 

  47. Gavrilov LA, Gavrilova NS (2002) Evolutionary theories of aging and longevity. Sci World J 2:339–356. https://doi.org/10.1100/tsw.2002.96

    Article  Google Scholar 

  48. Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization, Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005. Pasadena, CA, USA 2005:68–75. https://doi.org/10.1109/SIS.2005.1501604

    Article  Google Scholar 

  49. Liang JJ, Qu BY, Suganthan PN, Chen Q (2014) Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University, Singapore

  50. Awad NH, Ali MZ, Suganthan PN, Liang JJ, Qu BY (2016) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Technical Report, Nanyang Technological University, Singapore

  51. Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18. https://doi.org/10.1162/106365603321828970

    Article  Google Scholar 

  52. Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  53. Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18. https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

  54. Chen D, Zou F, Lu R, Wang P (2017) Learning backtracking search optimisation algorithm and its application. Inf Sci 376:71–94. https://doi.org/10.1016/j.ins.2016.10.002

    Article  Google Scholar 

  55. Kannan BK, Kramer SN (1994) An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des 116(2):405–411. https://doi.org/10.1115/1.2919393

    Article  Google Scholar 

  56. Arora J, Arora J (2011) Introduction to optimum design. McGraw-Hill, New York

    Google Scholar 

  57. Bhowmick A, Yadav K, Roy SD, Kundu S (2017) Throughput of an energy harvesting cognitive radio network based on prediction of primary user. IEEE Trans Veh Technol 66(9):8119–8128. https://doi.org/10.1109/TVT.2017.2690675

    Article  Google Scholar 

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Acknowledgments

This work is supported by Ministry of Electronics and Information Technology, Govt. of India (Reference Grant No.: 21(1)/2015-CC&BT).

Funding

This work is funded by Ministry of Electronics and Information Technology, Govt. of India (Grant No.: 21(1)/2015-CC&BT.)

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Silchar, Assam, India

    Sanjoy Debnath, Srimanta Baishya & Wasim Arif

  2. G.S. Sanyal School of Tele-Communication, IIT Kharagpur, Kharagpur, West Bengal, India

    Debarati Sen

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  1. Sanjoy Debnath
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Correspondence to Sanjoy Debnath.

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Debnath, S., Baishya, S., Sen, D. et al. A hybrid memory-based dragonfly algorithm with differential evolution for engineering application. Engineering with Computers 37, 2775–2802 (2021). https://doi.org/10.1007/s00366-020-00958-4

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