Nature inspired meta heuristic algorithms for optimization problems


Optimization and decision making problems in various fields of engineering have a major impact in this current era. Processing time and utilizing memory is very high for the currently available data. This is due to its size and the need for scaling from zettabyte to yottabyte. Some problems need to find solutions and there are other types of issues that need to improve their current best solution. Modelling and implementing a new heuristic algorithm may be time consuming but has some strong primary motivation - like a minimal improvement in the solution itself can reduce the computational cost. The solution thus obtained was better. In both these situations, designing heuristics and meta-heuristics algorithm has proved it’s worth. Hyper heuristic solutions will be needed to compute solutions in a much better time and space complexities. It creates a solution by combining heuristics to generate automated search space from which generalized solutions can be tuned out. This paper provides in-depth knowledge on nature-inspired computing models, meta-heuristic models, hybrid meta heuristic models and hyper heuristic model. This work’s major contribution is on building a hyper heuristics approach from a meta-heuristic algorithm for any general problem domain. Various traditional algorithms and new generation meta heuristic algorithms has also been explained for giving readers a better understanding.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. 1.

    Niu B, Wang H (2012) Bacterial colony optimization. Discrete Dyn Nat Soc.

    MathSciNet  Article  MATH  Google Scholar 

  2. 2.

    Maniezzo V, Gambardella LM, de Luigi F (2004) Ant Colony Optimization. In: New optimization techniques in engineering. Studies in fuzziness and soft computing, Springer, vol 141, Germany.

  3. 3.

    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06. Erciyes University, Engineering Faculty, Computer Engineering Department

  4. 4.

    Yang XS (2010) A new metaheuristic bat-inspired algorithm, nature inspired cooperative strategies for optimization, studies in computational intelligence, vol 284. Springer, Germany.

    Article  Google Scholar 

  5. 5.

    Hedayatzadeh R, Salmassi F Akhavan, Keshtgari M, Akbari R, Ziarati K (2010) Termite colony optimization: a novel approach for optimizing continuous problems. In: 2010 18th Iranian conference on electrical engineering, Isfahan, pp 553–558,

  6. 6.

    Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154.

    MathSciNet  Article  Google Scholar 

  7. 7.

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

    MathSciNet  Article  Google Scholar 

  8. 8.

    Saju Sankar S, Vinod Chandra SS (2020) A multi-agent ACO algorithm for effective vehicular traffic management system. Lect Notes Comput Sci 12145:640–647

    Article  Google Scholar 

  9. 9.

    Saju Sankar S, Vinod Chandra SS (2020) An ant colony optimization algorithm based automated generation of software test cases. Lect Notes Comput Sci 12145:231–239

    Article  Google Scholar 

  10. 10.

    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95 - international conference on neural networks, pp 1942–1948, vol 4, Australia.

  11. 11.

    Vinod Chandra SS, Saju Sankar S, Anand HS (2020) Multi-objective particle swarm optimization for cargo packaging. Lect Notes Comput Sci 12145:415–422

    Article  Google Scholar 

  12. 12.

    Saritha R, Vinod Chandra SS (2016) An approach using particle swarm optimization and rational kernel for variable length data sequence optimization. Lect Notes Comput Sci 9712:401–409

    Article  Google Scholar 

  13. 13.

    Reynolds RG (1994) An introduction to cultural algorithms. In: Sebald AV, Fogel LJ (eds), Proceedings of the third annual conference on evolutionary programming, pp 131–139. World Scientific, River Edge

  14. 14.

    Woo Z, Hoon J, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68.

    Article  Google Scholar 

  15. 15.

    Pham D, Ghanbarzadeh A, Koç E, Otri S, Rahim S, Zaidi MB (2005) The Bees Algorithm Technical Note, Manufacturing Engineering Centre, Cardiff University, pp 1-57, UK

  16. 16.

    Saritha R, Vinod Chandra SS (2018) Multi modal foraging by honey bees toward optimizing profits at multiple colonies. IEEE Intell Syst 34:14–22

    Article  Google Scholar 

  17. 17.

    Saritha R, Vinod Chandra SS (2017) Multi dimensional honey bee foraging algorithm based on optimal energy consumption. J Inst Eng Ser B 98:517–525

    Article  Google Scholar 

  18. 18.

    Krishnanand KN, Ghose D (2009) Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell 3:87–124.

    Article  Google Scholar 

  19. 19.

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

  20. 20.

    Rabanal P, Rodríguez I, Rubio F (2007) Using river formation dynamics to design heuristic algorithms. In: Unconventional computation, lecture notes in computer science, vol 4618. Springer, Germany.

  21. 21.

    Shah-Hosseini H (2008) Intelligent water drops algorithm: a new optimization method for solving the multiple knapsack problem. Int J Intell Comput Cybern 1:193–212.

    MathSciNet  Article  MATH  Google Scholar 

  22. 22.

    Yang XS (2009) Firefly algorithms for multimodal optimization. In: Lecture notes in computer science, vol 5792. Springer, Germany.

  23. 23.

    Rashedi E, Nezamabadi-pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9:727–745.

    MathSciNet  Article  MATH  Google Scholar 

  24. 24.

    Yang X, Deb Suash (2009) Cuckoo search via levy flights. In: World congress on nature and biologically inspired computing (NaBIC), pp 210–214, India.

  25. 25.

    Benasla L, Belmadani A, Mostefa R (2014) Spiral optimization algorithm for solving combined economic and emission dispatch. Int J Electr Power Energy Syst 62:163–174.

    Article  Google Scholar 

  26. 26.

    Anathalakshmi Ammal R, Sajimoan PC, Vinod Chandra SS (2020) Termite inspired algorithm for traffic engineering in hybrid software defined networks. PeerJ Comput Sci 6:283

    Article  Google Scholar 

  27. 27.

    Yang XS (2012) Flower pollination algorithm for global optimization. In: Lecture notes in computer science, vol 7445. Springer, Germany.

  28. 28.

    Cuevas E, Cienfuegos M (2014) A new algorithm inspired in the behavior of the social-spider for constrained optimization. Exp Syst Appl 41:412–425.

    Article  Google Scholar 

  29. 29.

    Eesa AS, Brifcani AMA, Orman Z (2013) Cuttlefish algorithm: a novel bio-inspired optimization algorithm. Int J Sci Eng Res 4(9):1978–1986

    Google Scholar 

  30. 30.

    Vinod Chandra SS (2016) Smell detection agent based optimization algorithm. J Inst Eng India Ser B 97:431–436.

    Article  Google Scholar 

  31. 31.

    Saju Sankar S, Vinod Chandra SS (2020) A structural testing model using SDA algorithm. Lect Notes Comput Sci 12145:405–412

    Article  Google Scholar 

  32. 32.

    Ananthalakshmi Ammal R, Sajimon PC, Vinod Chandra SS (2017) Application of smell detection agent based algorithm for optimal path identification by SDN Ccntrollers. Lect Notes Comput Sci 10386:502–510

    Article  Google Scholar 

  33. 33.

    Odili J, Kahar M, Nizam M, Shahid A (2015) African buffalo optimization a swarm-intelligence technique. Proc Comput Sci.

    Article  Google Scholar 

  34. 34.

    Biyanto TR, Fibrianto HY, Nugroho G, Hatta AM, Listijorini E, Budiati T, Huda H (2016) Duelist algorithm: an algorithm inspired by how duelist improve their capabilities in a duel. In: International conference in swarm intelligence, Springer, pp 39–47

  35. 35.

    Biyanto TRM, Irawan S, Febrianto HY, Afdanny N, Rahman AH, Gunawan KS, Pratama Januar AD, Bethiana Titania N (2017) Killer whale algorithm: an algorithm inspired by the life of killer whale. Proc Comput Sci 124:151–157

    Article  Google Scholar 

  36. 36.

    Wedyan A, Whalley J, Narayanan A (2017) Hydrological cycle algorithm for continuous optimization problems. J Optim.

    MathSciNet  Article  MATH  Google Scholar 

  37. 37.

    Jain M, Maurya S, Rani A, Singh V, Thampi SM, El-Alfy E-SM, Mitra S, Trajkovic L (2018) Owl search algorithm: a novel nature-inspired heuristic paradigm for global optimization. J Intell Fuzzy Syst 34:1573–1582

    Article  Google Scholar 

  38. 38.

    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 

  39. 39.

    RezaFathollahi-Fard AM, Hajiaghaei-Keshteli M, Tavakkoli-Moghaddam RezaTavakkoli-Moghaddam RR (2018) The social engineering optimizer (SEO). Eng Appl Artif Intell 72:267–293

    Article  Google Scholar 

  40. 40.

    Elsisi M (2019) Future search algorithm for optimization. Evol Intell 12(1):21–31

    Article  Google Scholar 

  41. 41.

    Harifi S, Khalilian M, Mohammadzadeh J, Ebrahimnejad S (2019) Emperor penguins colony: a new metaheuristic algorithm for optimization. Evol Intell 12:211–226

    Article  Google Scholar 

  42. 42.

    Kaveh A, Dadras AA (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84

    Article  Google Scholar 

  43. 43.

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

    Article  Google Scholar 

  44. 44.

    Askari Q, Younas I, Saeed M (2020) Political optimizer: a novel socio-inspired meta-heuristic for global optimization. Knowl Based Syst 195:105709

    Article  Google Scholar 

  45. 45.

    Askari Q, Saeed M, Younas I (2020) Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Exp Syst Appl 161:113

    Article  Google Scholar 

  46. 46.

    Zaeimi M, Ghoddosian A (2020) Color harmony algorithm: an art-inspired metaheuristic for mathematical function optimization. Soft Comput 24(16):12027–12066

    Article  Google Scholar 

  47. 47.

    Harifi S, Mohammadzadeh J, Khalilian M, Ebrahimnejad S (2020) Giza pyramids construction: an ancient-inspired metaheuristic algorithm for optimization. Evol Intell 2020:1–19

    Google Scholar 

  48. 48.

    Vinod Chandra SS, Anand HS, Saju Sankar S (2020) Optimal reservoir optimization using multiobjective genetic algorithm. Lect Notes Comput Sci 12145:445–454

    Article  Google Scholar 

  49. 49.

    Burke EK, Gendreau M, Hyde M, Kendall G, Ochoa G, Özcan E, Qu R (2013) Hyper heuristics: a survey of the state of the art. J Oper Res Soc 64(12):1695–1724

    Article  Google Scholar 

  50. 50.

    Gómez RH, Coello CAC (2017) A hyper heuristic of scalarizing functions. In: Proceedings of the genetic and evolutionary computation conference, pp 577–584

  51. 51.

    Hansen P, Mladenovic N, Pérez JAM (2010) Variable neighbourhood search: methods and applications. Ann Oper Res 175(1):367–407

    MathSciNet  Article  Google Scholar 

  52. 52.

    Uludag G, Kiraz B, Etaner-Uyar AS, Ozcan E (2013) A hybrid multi population framework for dynamic environments combining online and offline learning. Soft Comput 17(12):2327–2348

    Article  Google Scholar 

  53. 53.

    Hsiao P-C, Chiang T-C, Fu L-C (2012) A vns based hyper heuristic with adaptive computational budget of local search. In: Proceedings of the IEEE congress on evolutionary computation, pp 1–8

  54. 54.

    Meignan D (2011) An evolutionary programming hyper heuristic with co-evolution. In: Proceedings of the 53rd annual conference of the UK operational research society

  55. 55.

    Lehrbaum A, Musliu N (2012) A new hyper heuristic algorithm for cross do main search problems. In: Proceedings of the learning and intelligent optimization, LNCS, pp 437–442

  56. 56.

    Salcedo-Sanz S, Matías-Román J, Jiménez-Fernández S, Portilla-Figueras A, Cuadra L (2014) An evolutionary based hyper heuristic approach for the jaw breaker puzzle. Appl Intell 40(3):404–414

    Article  Google Scholar 

  57. 57.

    Salhi A, Rodríguez JAV (2014) Tailoring hyper heuristics to specific instances of a scheduling problem using affinity and competence functions. Memetic Comput 6(2):77–84

    Article  Google Scholar 

  58. 58.

    Strickler A, Lima JAP, Vergilio SR, Pozo AT (2016) Deriving products for variability test of feature models with a hyper heuristic approach. Appl Soft Comput 49:1232–1242

    Article  Google Scholar 

  59. 59.

    Parejo JA, Ruiz-Cortés A, Lozano S, Fernandez P (2012) Metaheuristic optimization frameworks: a survey and benchmarking. Soft Comput 16(3):527–561

    Article  Google Scholar 

  60. 60.

    Tyasnurita R, Özcan E, John R (2017) Learning heuristic selection using a time delay neural network for open vehicle routing. In: Proceedings of the IEEE congress on evolutionary computation, pp 1474–1481

  61. 61.

    Vinod Chandra SS, Anand HS (2021) Phototropic algorithm for global optimisation problems. Applied Intelligence

Download references


The authors would like to thank the Government for India for providing Copyrights (IPR) for the Nature Inspired Algorithm developed by the authors (Registration Nos.: L-74114/2018, L-65846/2017, L-62609/2015, and L-60823/2014). The authors also extend their thanks to all the Machine Intelligent Research group, who have been a constant support during the various phases of analysis and implementation of different nature inspired algorithms.

Author information



Corresponding author

Correspondence to Vinod Chandra S. S..

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 83 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

S. S., V.C., H. S., A. Nature inspired meta heuristic algorithms for optimization problems. Computing (2021).

Download citation


  • Nature inspired computing
  • Meta heuristics
  • Hyper heuristics
  • Evolutionary computing
  • Bio inspired computing
  • Hybrid meta heuristics

Mathematics Subject Classification

  • 90C59