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An improved hybrid mayfly algorithm for global optimization

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Abstract

As the complexity of optimization problems increases, metaheuristic algorithms play an important role in dealing with complex computational problems and try to find the best solution from all feasible solutions to the problem. The mayfly algorithm is a novel metaheuristic algorithm based on the social behavior of biological groups. The algorithm achieves global and local search by simulating the flight behavior and mating process of mayflies to obtain global optimal solution. However, the traditional mayfly algorithm has problems such as low convergence accuracy and poor stability and is prone to becoming trapped in local optimality. Aiming at the problem of the mayfly algorithm, an improved mayfly algorithm combined with the gray wolf optimization algorithm (MA-GWO) is proposed. In the mayfly algorithm, the Lévy flight strategy and the hunting mechanism of the gray wolf optimization algorithm are introduced to achieve complementary advantages. To verify the superiority of the proposed algorithm, 19 classical benchmark functions, CEC-C06 2019 test functions and 5 engineering design problems are compared with various advanced metaheuristic algorithms. The experimental data show that the MA-GWO algorithm has significant enhancements over the traditional mayfly algorithm. In several test cases, especially for high-dimensional optimization problems, the MA-GWO algorithm is far superior to other metaheuristic algorithms, has better convergence and stability, and is an effective and feasible algorithm.

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References

  1. Zervoudakis K, Tsafarakis S (2022) A global optimizer inspired from the survival strategies of flying foxes. Eng Comput. https://doi.org/10.1007/s00366-021-01554-w

    Article  Google Scholar 

  2. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, IEEE, 4: 1942–1948

  3. Yang XS (2010) A new metaheuristic bat-inspired algorithm[M]//Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, pp 65–74

    Google Scholar 

  4. Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2022) Reptile Search Algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191(11):116158

    Google Scholar 

  5. Naruei I, Keynia F, Molahosseini AS (2022) Hunter-prey optimization: algorithm and applications. Soft Comput 26(3):1279–1314

    Google Scholar 

  6. Wang GH, Yuan YL, Guo WW (2019) An improved rider optimization algorithm for solving engineering optimization problems. IEEE ACCESS 7:80570–80576

    Google Scholar 

  7. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  8. Holland JH (1992) Genetic algorithms. Sci Am 267:66–72

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  10. Rechenberg I (1978) Evolutionsstrategien. Springer, Berlin Heidelberg, pp 83–114

    Google Scholar 

  11. Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics-based algorithm. Futur Gener Comput Syst 101:646–667

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  13. Tan Y, Zhu YC (2010) Fireworks algorithm for optimization. Adv Swarm Intell 6145:355–364

    Google Scholar 

  14. Nematollahi AF, Rahiminejad A, Vahidi B (2017) A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization. Appl Soft Comput 59:596–621

    Google Scholar 

  15. Shareef H, Ibrahim AA, Mutlag AH (2015) Lightning search algorithm. Appl Soft Comput 36:315–333

    Google Scholar 

  16. Anita YA (2019) AEFA: artificial electric field algorithm for global optimization. Swarm Evol Comput 48:93–108

    Google Scholar 

  17. Bouchekara H (2019) Electrostatic discharge algorithm: a novel nature-inspired optimisation algorithm and its application to worst-case tolerance analysis of an EMC filter. IET Sci Meas Technol 13(4):491–499

    Google Scholar 

  18. Zheng YJ (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11

    MathSciNet  MATH  Google Scholar 

  19. Yang XS (2010) Fireflfly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspired Comput 2(2):78–84

    Google Scholar 

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

    Google Scholar 

  21. Xie L, Han T, Zhou H, Zhang ZR, Han B, Tang AD (2021) Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Computational Intelligence and Neuroscience, 2021

  22. Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Google Scholar 

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

    Google Scholar 

  24. He S, Wu QH, Saunders JR (2006) "A Novel Group Search Optimizer Inspired by Animal Behavioural Ecology," 2006 IEEE International Conference on Evolutionary Computation, pp. 1272–1278

  25. Moosavian N, Roodsari BK (2014) Soccer league competition algorithm: a novel meta-heuristic algorithm for optimal design of water distribution networks. Swarm Evol Comput 17:14–24

    Google Scholar 

  26. Talatahari S, Bayzidi H, Saraee M (2021) Social network search for global optimization. IEEE ACCESS 9:92815–92863

    Google Scholar 

  27. Shi YH (2011) Brain Storm Optimization Algorithm. Paper presented at the 2nd International Conference on Swarm Intelligence (ICSI), Chongqing, Peoples R China,1 pp 303–309

  28. Binu D, Kariyappa BS (2019) RideNN: a new rider optimization algorithm-based neural network for fault diagnosis in analog circuits. IEEE Trans Instrum Meas 68(1):2–26

    Google Scholar 

  29. Zervoudakis K, Tsafarakis S (2020) A mayfly optimization algorithm. Comput Ind Eng 145:106559

    Google Scholar 

  30. Bhattacharyya T, Chatterjee B, Singh PK, Yoon JH, Geem ZW, Sarkar R (2020) Mayfly in harmony: a new hybrid meta-heuristic feature selection algorithm. IEEE Access 8:195929–195945

    Google Scholar 

  31. Zhao J, Gao ZM, Ieee (2020) The fully informed mayfly optimization algorithm. Paper presented at the International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE), Chengdu, PEOPLES R CHINA

  32. Gao ZM, Li SR, Zhao J, Hu YR, Ieee (2020) Self-organizing hierarchical mayfly optimization algorithm. Paper presented at the International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE), Chengdu, Peoples R China

  33. He XM, He BN, Zhao YW, Cui RX, Zhang JR, Dong YC, Jiang RZ (2021) MPPT control based on improved mayfly optimization algorithm under complex shading conditions. Int J Emerg Electr Power Syst 22(6):661–674

    Google Scholar 

  34. Gupta J, Nijhawan P, Ganguli S (2021) Parameter estimation of fuel cell using chaotic mayflies optimization algorithm. Adv Theory Simul 4(12):2100183

    Google Scholar 

  35. Owoola EO, Xia KW, Wang T, Umar A, Akindele RG (2021) Pattern synthesis of uniform and sparse linear antenna array using mayfly algorithm. IEEE Access 9:77954–77975

    Google Scholar 

  36. Guo XK, Yan XG, Jermsittiparsert K (2021) Using the modified mayfly algorithm for optimizing the component size and operation strategy of a high temperature PEMFC-powered CCHP. Energy Rep 7:1234–1245

    Google Scholar 

  37. Sridharan S, Prabhu VV, Velmurugan P (2021) Efficient maximum power point tracking in grid connected switched reluctance generator in wind energy conversion system: an enhanced Mayfly algorithm transient search optimization. Energy Sources Part a-Recovery Utilization and Environmental Effects

  38. Jain, A., & Gupta, A (2022) Review on Recent Developments in the Mayfly Algorithm. Paper presented at the Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences, Singapore

  39. Gao ZM, Zhao J, Li SR, Hu YR (2020) The improved mayfly optimization algorithm. J Phys: Conf Ser 1684(1):012077

    Google Scholar 

  40. Zhang H, Liu Z, Gui SW, Zou M, Wang PY Improved mayfly algorithm based on hybrid mutation. Electronics letters

  41. Jiang YX, Wu Q, Zhu SK, Zhang LK (2022) Orca predation algorithm: a novel bio-inspired algorithm for global optimization problems. Expert Syst Appl 188:116206

    Google Scholar 

  42. Barthelemy P, Bertolotti J, Wiersma DS (2008) A lévy flight for light. Nature 453(7194):495–498

    Google Scholar 

  43. Long W, Liang XM, Cai SH, Jiao JJ, Zhang WZ (2017) A modified augmented Lagrangian with improved grey wolf optimization to constrained optimization problems. Neural Comput Appl 28:S421–S438

    Google Scholar 

  44. Liu Z, Jiang P, Wang JZ, Zhang LF (2021) Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm. Expert Syst Appl 177:114974

    Google Scholar 

  45. Li MD, Xu GH, Lai Q, Chen J (2022) A chaotic strategy-based quadratic opposition-based learning adaptive variable-speed whale optimization algorithm. Math Comput Simul 193:71–99

    MathSciNet  MATH  Google Scholar 

  46. Ling Y, Zhou YQ, Luo QF (2017) Levy flight trajectory-based whale optimization algorithm for global optimization. IEEE ACCESS 5:6168–6186

    Google Scholar 

  47. Yan ZP, Zhang JZ, Zeng J, Tang JL (2021) Nature-inspired approach: an enhanced whale optimization algorithm for global optimization. Math Comput Simul 185:17–46

    MathSciNet  MATH  Google Scholar 

  48. Zhang J, Wang JS (2020) Improved salp swarm algorithm based on levy flight and sine cosine operator. IEEE ACCESS 8:99740–99771

    Google Scholar 

  49. Zhang XM, Lin QY, Mao WT, Liu SW, Dou Z, Liu GQ (2021) Hybrid particle swarm and grey wolf optimizer and its application to clustering optimization. Appl Soft Comput 101:107061

    Google Scholar 

  50. Zhang YY, Jin ZG, Chen Y (2020) Hybrid teaching-learning-based optimization and neural network algorithm for engineering design optimization problems. Knowl-Based Syst 187:104836

    Google Scholar 

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

    MATH  Google Scholar 

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

    Google Scholar 

  53. Wilcoxon F (1947) Individual comparisons by ranking methods. Biom Bull 1(6):80–83

    MathSciNet  Google Scholar 

  54. Luo QF, Yang X, Zhou YQ (2019) Nature-inspired approach: an enhanced moth swarm algorithm for global optimization. Math Comput Simul 159:57–92

    MathSciNet  MATH  Google Scholar 

  55. Abdullah JM, Ahmed T (2019) Fitness dependent optimizer: inspired by the bee swarming reproductive process. IEEE ACCESS 7:43473–43486

    Google Scholar 

  56. Chickermane H, Gea H (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39:829–846

    MathSciNet  MATH  Google Scholar 

  57. Arora JS (1989) Introduction to optimum design. McGraw-Hill, New York

    Google Scholar 

  58. Gold S, Krishnamurty S (1997) Trade-offs in robust engineering design. In: Paper presented at the proceeding of the 1997 ASME design engineering technical conferences, Sacramento

  59. Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 112:223–229

    Google Scholar 

  60. Nowcki H (1974) Optimization in pre-contract ship design. In: Fujita Y, Lind K, Williams TJ (eds) Computer applications in the automation of shipyard operation and ship design, vol 2. North Holland. Elsevier, New York, pp 327–338

    Google Scholar 

  61. Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014

    Google Scholar 

  62. Coello CAC (2000) Constraint-handling using an evolutionary multiobjective optimization technique. Civ Eng Syst 17(4):319–346

    Google Scholar 

  63. Huang FZ, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356

    MathSciNet  MATH  Google Scholar 

  64. Ray T, Liew KM (2003) Society and civilization: An optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput 7(4):386–396

    Google Scholar 

  65. Baykasoglu A, Ozsoydan FB (2015) Adaptive firefly algorithm with chaos for mechanical design optimization problems. Appl Soft Comput 36:152–164

    Google Scholar 

  66. Cheng MY, Prayogo D (2017) A novel fuzzy adaptive teaching-learning-based optimization (FATLBO) for solving structural optimization problems. Eng Comput 33(1):55–69

    Google Scholar 

  67. Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612

    Google Scholar 

  68. Zhang M, Luo W, Wang XF (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178(15):3043–3074

    Google Scholar 

  69. Tsai JF (2005) Global optimization of nonlinear fractional programming problems in engineering design. Eng Opt 37(4):399–409

    MathSciNet  Google Scholar 

  70. Ray T, Saini P (2001) Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Opt 33(6):735–748

    Google Scholar 

  71. Liu H, Cai ZX, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10(2):629–640

    Google Scholar 

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

    Google Scholar 

  73. Tsai HC (2015) Roach infestation optimization with friendship centers. Eng Appl Artif Intell 39:109–119

    Google Scholar 

  74. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Google Scholar 

  75. Wang GG (2003) Adaptive response surface method using inherited Latin hypercube design points. J Mech Des 125(2):210–220

    Google Scholar 

  76. Cheng MY, Prayogo D (2014) Symbiotic Organisms Search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    Google Scholar 

  77. Dai CY, Hu ZB, Li Z, Xiong ZG, Su QH (2020) An improved grey prediction evolution algorithm based on topological opposition-based learning. IEEE ACCESS 8:30745–30762

    Google Scholar 

  78. Kannan B, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des 116:405–411

    Google Scholar 

  79. Deb K, Goyal M (1996) A combined genetic adaptive search (GeneAS) for engineering design. Comput Sci Inform 26:30–45

    Google Scholar 

  80. Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183

    Google Scholar 

  81. Chegini SN, Bagheri A, Najafi F (2018) PSOSCALF: a new hybrid PSO based on sine cosine algorithm and levy flight for solving optimization problems. Appl Soft Comput 73:697–726

    Google Scholar 

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Acknowledgements

The authors would like to thank the editors and reviewers whose feedback has greatly contributed to the improvement of this work. This article is supported by the National Nature Science Foundation of China (No. 52071102).

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  1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China

    Zheping Yan, Jinyu Yan, Yifan Wu & Chao Zhang

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  1. Zheping Yan
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Yan, Z., Yan, J., Wu, Y. et al. An improved hybrid mayfly algorithm for global optimization. J Supercomput 79, 5878–5919 (2023). https://doi.org/10.1007/s11227-022-04883-9

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