Skip to main content
SpringerLink
Log in

Seasons optimization algorithm

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

This paper introduces a new stochastic bio-inspired optimization algorithm, denoted as seasons optimization (SO) algorithm. This algorithm is inspired by the growth cycle of trees in different seasons of a year. It is an iterative and population-based algorithm working with a population of initial solutions known as a forest. Each individual in the forest is referred to as a tree. Until the termination conditions are satisfied, the trees in the forest are updated to a new generation by applying four operators similar to the trees’ life cycles in nature: renew, competition, seeding, and resistance. These operators hopefully cause the trees to converge towards the global optimum of the optimization problem. The effectiveness of the proposed SO algorithm is evaluated using multi-variable single-objective test problems and compared with several well-known baseline and state-of-the-art algorithms. The results show that the proposed algorithm outperformed its counterparts in terms of solution quality and finding the global optimum on most benchmark functions.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

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

    MathSciNet  MATH  Google Scholar 

  2. Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144

    MathSciNet  MATH  Google Scholar 

  3. 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 

  4. Darwish A (2018) Bio-inspired computing: algorithms review, deep analysis, and the scope of applications. Future Comput Inform J 3(2):231–246

    MathSciNet  Google Scholar 

  5. Wang Y, Yang Y, Cao Sh, Zhang X, Gao Sh (2020) A review of applications of artificial intelligent algorithms in wind farms. Artif Intell Rev 53(5):3447–3500

    Google Scholar 

  6. Puchinger J, Raidl R (2005) Combining metaheuristics and exact algorithms in combinatorial optimization: a survey and classification. In: Artificial intelligence and knowledge engineering applications: a bioinspired approach, Lecture notes in computer science, vol 3562. Springer, Heidelberg, pp 41–53

  7. Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inform 19(1):43–53

    Google Scholar 

  8. Krause J, Cordeiro J (2013) A survey of swarm algorithms applied to discrete optimization problems. In: Swarm intelligence and bio-inspired computation. Elsevier, pp 169–191. https://doi.org/10.1016/B978-0-12-405163-8.00007-7

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

    MathSciNet  MATH  Google Scholar 

  10. 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

    MathSciNet  Google Scholar 

  11. Alsalibi B, Venkat I, Subramanian KG (2015) The impact of bio-inspired approaches toward the advancement of face recognition. ACM Comput Surv 48(1):1–33

    Google Scholar 

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

    Google Scholar 

  13. Hussain K, Salleh M, Cheng S, Shi Y (2018) Metaheuristic research: a comprehensive survey. Artif Intell Rev 52(4):2191–2233

    Google Scholar 

  14. Lim SM, Leong KY (2018) A brief survey on intelligent swarm-based algorithms for solving optimization problems. In: Nature-inspired methods for stochastic, robust and dynamic optimization. IntechOpen, pp 47–61. https://doi.org/10.5772/intechopen.76979

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

    Google Scholar 

  16. Engelbrecht AP (2007) Computational intelligence, an introduction. Wiley, Hoboken

    Google Scholar 

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

    MATH  Google Scholar 

  18. Gao S, Member S, Yu Y, Wang Y, Wang J (2019) Chaotic local search-based differential evolution algorithms for optimization. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2019.2956121

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  20. Igel C, Hansen N, Roth S (2007) Covariance matrix adaptation for multi-objective optimization. Evol Comput 15(1):1–28

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  23. Kumar M, Kulkarni AJ, Satapathy SC (2018) Socio evolution & learning optimization algorithm: a socio-inspired optimization methodology. Future Gener Comput Syst 81:252–272

    Google Scholar 

  24. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the IEEE congress on evolutionary computation, 25–28 September 2007, Singapore, pp 4661–4667

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

    MathSciNet  MATH  Google Scholar 

  26. Yang X, Deb S (2009) Cuckoo search via Levy flights. In: 2009 World Congress on nature and biologically inspired computing (NaBIC 2009). Coimbatore, India, pp 210–214

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

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  32. 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

    Google Scholar 

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

    Google Scholar 

  34. Huan TT, Kulkarni AJ, Kanesan J, Huang CJ, Abraham A (2016) Ideology algorithm: a socio-inspired optimization methodology. Neural Comput Appl 28(1):845–876

    Google Scholar 

  35. 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 

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

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  39. Rashedi E, Saryazdi Nezamabadi-pour HS (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    MATH  Google Scholar 

  40. Lam AYS, Li VOK (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14(3):381–399

    Google Scholar 

  41. Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37:106–111

    Google Scholar 

  42. Biswas A, Mishra KK, Tiwari S, Misra AK (2013) Physics-inspired optimization algorithms: a survey. J Optim 2013:1–16

    Google Scholar 

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

    Google Scholar 

  44. Du H, Wu X, Zhuang J (2006) Small-world optimization algorithm for function optimization. Advances in natural computation. Springer, Berlin, Heidelberg, pp 264–273

    Google Scholar 

  45. Shah-Hosseini H (2007) Problem solving by intelligent water drops. In: 2007 IEEE congress on evolutionary computation (CEC), Singapore, 25–28 September 2007, pp 3226–3231

  46. Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(4):267–289

    MATH  Google Scholar 

  47. Shah-hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6(2):132–140

    Google Scholar 

  48. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112:283–294

    Google Scholar 

  49. Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184

    MathSciNet  Google Scholar 

  50. Wang Y, Gao Sh, Yu Y, Wang Z, Cheng J, Yuki T (2020) A gravitational search algorithm with chaotic neural oscillators. IEEE Access 8:25938–25948

    Google Scholar 

  51. Lowman MD, Rinker HB (2004) Forest canopies, 2nd edn. Elsevier/Academic Press, Tokyo

    Google Scholar 

  52. Gosling P (2007) Raising trees and shrubs from seed. Forestry Commission, Edinburgh

    Google Scholar 

  53. Wohlleben P (2016) The hidden life of trees: what they feel, how they communicate- Discoveries from a secret world. Greystone Books, Vancouver

    Google Scholar 

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

    Google Scholar 

  55. Contreras MA, Affleck D, Chung W (2011) Forest ecology and management evaluating tree competition indices as predictors of basal area increment in western Montana forests. For Ecol Manag 262(11):1939–1949

    Google Scholar 

  56. Rouvinen S, Kuuluvainen T (1997) Structure and asymmetry of tree crowns in relation to local competition in a natural mature Scots pine forest. Can J For Res 27(6):890–902

    Google Scholar 

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

    Google Scholar 

  58. Charrier G, Cochard H, Améglio T (2013) Evaluation of the impact of frost resistances on potential altitudinal limit of trees. Tree Physiol 33(9):891–902

    Google Scholar 

  59. Charra-Vaskou K, Charrier G, Wortemann R, Beikircher B, Cochard H, Ameglio T, Mayr S (2012) Drought and frost resistance of trees: a comparison of four species at different sites and altitudes. Ann For Sci 69(3):325–333

    Google Scholar 

  60. 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, Technical Report

  61. 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. In: Proceedings of the IEEE congress on evolutionary computation (CEC), Sendai, Japan, 25–28 May 2015

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

  63. Jamil M, Yang XS (2013) A literature survey of benchmark functions for global optimisation problems. Int J Math Model Numer Optim 4(2):150–194

    MATH  Google Scholar 

  64. Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. IEEE Trans Evol Comput 1(3):1785–1791

    Google Scholar 

  65. Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657

    Google Scholar 

  66. 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

    Google Scholar 

  67. Wang Y, Yu Y, Gao Sh, Pan H, Yang G (2019) A hierarchical gravitational search algorithm with an effective gravitational constant. Swarm Evol Comput 46:118–139

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Bonab, Bonab, Iran

    Hojjat Emami

Authors
  1. Hojjat Emami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hojjat Emami.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Emami, H. Seasons optimization algorithm. Engineering with Computers 38, 1845–1865 (2022). https://doi.org/10.1007/s00366-020-01133-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01133-5

Keywords

Search

Navigation