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Hazelnut tree search algorithm: a nature-inspired method for solving numerical and engineering problems

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Abstract

In this paper, a novel nature-inspired optimization algorithm, hazelnut tree search (HST) is proposed for solving numerical and engineering optimization problems. HST is a multi-agent algorithm that simulates the search process for finding the best hazelnut tree in a forest. The algorithm is composed of three main actuators: growth, fruit scattering, and root spreading. In the growth phase, trees compete with each other on shared resources to grow up and improve their fitness. In the fruit scattering phase, HTS performs exploration by simulating the movement of hazelnuts around the forest with the help of animals and rodents. In the root spreading, HTS performs exploitation by modeling the root spreading mechanism of trees around themselves. The performance of the proposed algorithm is evaluated on multi-variable unconstraint numerical optimization benchmarks and constraint engineering problems. Comparing the proposed algorithm with a few other optimization algorithms shows the superiority of the HTS in terms of problem-solving success and finding the global optimum on most benchmark problems.

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References

  1. Emami H, Derakhshan F (2015) Election algorithm: a new socio-politically inspired strategy. AI Commun 28(3):591–603

    Article  MathSciNet  MATH  Google Scholar 

  2. Beheshti Z, Mariyam S, Shamsuddin H (2013) A review of population-based meta-heuristic algorithms. Int J Adv Soft Comput Appl 5(1):1–35

    Google Scholar 

  3. Sotoudeh-anvari A, Hafezalkotob A (2018) A bibliography of metaheuristics-review from 2009 to 2015. Int J Knowl Intell Eng Syst 22:83–95

    Google Scholar 

  4. Boussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82–117

    Article  MathSciNet  MATH  Google Scholar 

  5. Das P, Das DK, Dey S (2018) A new class topper optimization algorithm with an application to data clustering. IEEE Trans Emerg Top Comput 6750:1–11

    Google Scholar 

  6. Thangaraj R, Pant M, Abraham A, Bouvry P (2011) Particle swarm optimization: hybridization perspectives and experimental illustrations. Appl Math Comput 217(12):5208–5226

    MATH  Google Scholar 

  7. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1:28–39

    Article  Google Scholar 

  8. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  9. Yang X (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio Inspired Comput 2(2):78–84

    Article  Google Scholar 

  10. Gandomia AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  MathSciNet  MATH  Google Scholar 

  11. Yu JJQ, Li VOK (2015) A social spider algorithm for global optimization. Appl Soft Comput J 30:614–627

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175

    Article  Google Scholar 

  15. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  16. Kirkpatrick S, Vecchi GCD, Science MP (1983) Optimization by simulated annealing. Science 220:671–680

    Article  MathSciNet  MATH  Google Scholar 

  17. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inform Sci 179(13):2232–48

    Article  MATH  Google Scholar 

  18. Tayarani M, Akbarzadeh M (2014) Magnetic-inspired optimization algorithms: operators and structures. Swarm Evol Comput 19:82–101

    Article  Google Scholar 

  19. Pereira J, Francisco MB, Diniz CA, Oliver GA, Cunha SS, Gomes GF (2021) Lichtenberg algorithm: a novel hybrid physics-based meta-heuristic for global optimization. Expert Syst Appl 170(2021):114522

    Article  Google Scholar 

  20. Pereira J, Francisco MB, Cunha SS, Gomes GF (2021) A powerful lichtenberg optimization algorithm: a damage identification case study. Eng Appl Artif Intell 97:104055

    Article  Google Scholar 

  21. Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley

  22. Merrikh-Bayat F (2015) The runner-root algorithm: a metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature. Appl Soft Comput J 33:292–303

    Article  Google Scholar 

  23. Ghaemia M, Feizi-Derakhshi MR (2014) Forest optimization algorithm. Expert Syst Appl 41(15):6676–6687

    Article  Google Scholar 

  24. Al-Betar MA, Awadallah MA, Abu Doush I, Hammouri AI, Mafarja M, Alyasseri ZAA (2012) Flower pollination algorithm for global optimization. Int Conf Unconvent Comput Natl Comput 2012:240–249

    Google Scholar 

  25. Shayanfar H, Soleimanian F (2018) Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput J 71:728–746

    Article  Google Scholar 

  26. Gomes GF, Da Cunha SS, Ancelotti AC (2019) A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Eng Comput 35(2):619–626

    Article  Google Scholar 

  27. Gomes GF, Almeida FA (2020) Tuning metaheuristic algorithms using mixture design: application of sunflower optimization for structural damage identification. Adv Eng Softw 149:102877

    Article  Google Scholar 

  28. Khan, M. S., ul Hassan, C. A., Sadiq, H. A., Ali, I., Rauf, A., & Javaid, N. (2017, August). A new meta-heuristic optimization algorithm inspired from strawberry plant for demand side management in smart grid. In International Conference on Intelligent Networking and Collaborative Systems (pp. 143-154). Springer, Cham.

  29. Mirjalili S, Gandomi AH, Zahra S, Saremi S (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:1–29

    Article  Google Scholar 

  30. Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31

    Article  Google Scholar 

  31. Hosseinalipour A, Soleimanian F, Masdari M, Khademi A (2021) A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology. Appl Intell: 1–36

  32. Barshandeh S, Haghzadeh M (2020) A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Eng. Comput: 1-44

  33. Barshandeh S, Piri F, Sangani, SR (2020) HMPA: an innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris Hawks optimization algorithms for engineering problems. Eng Comput: 1–45

  34. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  35. Sharma A, Sharma A, Panigrahi BK, Kiran D, Kumar R (2016) Ageist spider monkey optimization algorithm. Swarm Evol Comput 28:58–77

    Article  Google Scholar 

  36. Contreras MA, Affleck D, Chung W (2011) Evaluating tree competition indices as predictors of basal area increment in western Montana forests. For Ecol Manage 262(11):1939–1949

    Article  Google Scholar 

  37. Das A, Battles J, Stephenson NL, Van Mantgem PJ (2011) The contribution of competition to tree mortality in old-growth coniferous forests. For Ecol Manage 261(7):1203–1213

    Article  Google Scholar 

  38. Cain ML, Milligan BG, Strand AE (2000) Long-distance seed dispersal in plant populations. Am J Bot 87(9):1217–1227

    Article  Google Scholar 

  39. Howe HF, Smallwood J (1982) Ecology of seed dispersal. Annu Rev Ecol Syst 13:85–110

    Article  Google Scholar 

  40. Zeide B (1993) Analysis of growth equations. For Sci 39(3):594–616

    Article  Google Scholar 

  41. Yi L, Li H, Guo J, Deussen O, Zhang X (2018) Tree growth modeling constrained by growth equations. Comput Graph Forum 37(1):239–253

    Article  Google Scholar 

  42. Talatahari S, Farahmand Azar B, Sheikholeslami R, Gandomi AH (2012) Imperialist competitive algorithm combined with chaos for global optimization. Commun Nonlinear Sci Numer Simul 17(1):1312–1319

    Article  MathSciNet  MATH  Google Scholar 

  43. Suganthan P, Hansen N, Liang J, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC2005 special session on real parameter optimization. Nanyang Technological University, Tech. Rep

  44. Liu B, Chen Q, Zhang Q, Liang JJ, Suganthan PN, Qu BY (2013) Problem definitions and evaluation criteria for computationally expensive single objective numerical optimization. Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, December 2013

  45. Suganthan P, Ali M, Wu G, Mallipeddi R (2018) Special session and competitions on real-parameter single objective optimization. In: Proceedings of the IEEE congress on evolutionary computation (CEC), Rio de Janeiro, Brazil, Rep., Jul 2018

  46. Houssein EH, Saad MR, Hashim FA, Shaban H, Hassaballah M (2020) Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 94::103731

    Article  Google Scholar 

  47. Emami H (2020) Seasons optimization algorithm. Eng Comput. https://doi.org/10.1007/s00366-020-01133-5

  48. Yang X, Deb S (2009) Cuckoo search via Levy flights. 2009 World Congress on Nature and Biologically Inspired Computing (NaBIC 2009). Coimbatore, India, pp 210–214

  49. Chen Q, Liu B, Zhang Q, Liang J (2015) Evaluation criteria for CEC 2015 special session and competition on bound constrained single-objective computationally expensive numerical optimization. Sendai, Japan, 25–28 May

  50. Derrac J, García 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

    Article  Google Scholar 

  51. Heidari AA, Mirjalili SA, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Fut Gen Comput Syst 97:849–872

    Article  Google Scholar 

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  1. University of Bonab, Bonab, Iran

    Hojjat Emami

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Emami, H. Hazelnut tree search algorithm: a nature-inspired method for solving numerical and engineering problems. Engineering with Computers 38 (Suppl 4), 3191–3215 (2022). https://doi.org/10.1007/s00366-021-01460-1

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