Skip to main content
SpringerLink
Log in

Nature Inspired Metaheuristics and Their Applications in Agriculture: A Short Review

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11804))

Included in the following conference series:

Abstract

Nature inspired metaheuristics algorithms have been the target of several studies in the most varied scientific areas due to their high efficiency in solving real world problems. This is also the case of agriculture. Among the most well-established nature inspired metaheuristics the ones selected to be addressed in this work are the following: genetic algorithms, differential evolution, simulated annealing, harmony search, particle swarm optimization, ant colony optimization, firefly algorithm and bat algorithm. For each of them, the mechanism that inspired it and a brief description of its operation is presented, followed by a review of their most relevant agricultural applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adeyemo, J., Bux, F., Otieno, F.: Differential evolution algorithm for crop planning: single and multi-objective optimization model. Int. J. Phys. Sci. 5(10), 1592–1599 (2010)

    Google Scholar 

  2. Adeyemo, J., Otieno, F.: Differential evolution algorithm for solving multi-objective crop planning model. Agric. Water Manag. 97(6), 848–856 (2010)

    Article  Google Scholar 

  3. Akbari, R., Ziarati, K.: A multilevel evolutionary algorithm for optimizing numerical functions. Int. J. Industr. Eng. Comput. 2(2), 419–430 (2011)

    Google Scholar 

  4. Alaiso, S., Backman, J., Visala, A.: Ant colony optimization for scheduling of agricultural contracting work. IFAC Proc. Vol. 46(18), 133–137 (2013)

    Article  Google Scholar 

  5. Andersen, H.J., Reng, L., Kirk, K.: Geometric plant properties by relaxed stereo vision using simulated annealing. Comput. Electron. Agric. 49(2), 219–232 (2005)

    Article  Google Scholar 

  6. Bäck, T., Fogel, D., Michalewicz, Z.: Handbook of evolutionary computation. Release 97(1), B1 (1997)

    MATH  Google Scholar 

  7. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)

    Article  Google Scholar 

  8. Brezina Jr., I., Čičková, Z.: Solving the travelling salesman problem using the ant colony optimization. Manage. Inf. Syst. 16(4), 010–014 (2011)

    Google Scholar 

  9. Brooks, S.P., Morgan, B.J.: Optimization using simulated annealing. Statistician 44, 241–257 (1995)

    Article  Google Scholar 

  10. Brown, P.D., Cochrane, T.A., Krom, T.D.: Optimal on-farm irrigation scheduling with a seasonal water limit using simulated annealing. Agric. Water Manage. 97(6), 892–900 (2010)

    Article  Google Scholar 

  11. Brownlee, J.: Clever Algorithms: Nature-Inspired Programming Recipes. Jason Brownlee, Melbourne (2011)

    Google Scholar 

  12. Coelho, J., de Moura Oliveira, P., Cunha, J.B.: Greenhouse air temperature predictive control using the particle swarm optimisation algorithm. Comput. Electron. Agric. 49(3), 330–344 (2005)

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Dias, J.A.C., Machado, P., Pereira, F.C.: Privacy-aware ant colony optimization algorithm for real time route planning. In: Proceedings of the World Conference on Transport Research, p. 9 (2013)

    Google Scholar 

  15. Dorigo, M.: Optimization, learning, and natural algorithms. Ph.D. thesis, Politecnico di Milano, Milano (1992)

    Google Scholar 

  16. Dorigo, M., Birattari, M.: Ant colony optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-30164-8

  17. Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for discrete optimization. Artif. Life 5(2), 137–172 (1999)

    Article  Google Scholar 

  18. Dorigo, M., Stültze, T.: Ant Colony Optimization. The MIT Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  19. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43. IEEE (1995)

    Google Scholar 

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

    Google Scholar 

  21. Ferentinos, K.P., Tsiligiridis, T.A.: Adaptive design optimization of wireless sensor networks using genetic algorithms. Comput. Netw. 51(4), 1031–1051 (2007)

    Article  MATH  Google Scholar 

  22. Fister, I., Fister Jr., I., Yang, X.S., Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol. Comput. 13, 34–46 (2013)

    Article  Google Scholar 

  23. Fuchigami, H.Y.: Algoritmo simulated annealing para programação de flow shops paralelos proporcionais com tempo de setup (2011). www.din.uem.br/sbpo/sbpo2011/pdf/88031.pdf. Accessed 22 Mar 2019

  24. Geem, Z.W.: Recent Advances in Harmony Search Algorithm. Studies in Computational Intelligence, vol. 270. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-04317-8

    Book  MATH  Google Scholar 

  25. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)

    Article  Google Scholar 

  26. Glover, F.: Tabu search–part i. ORSA J. Comput. 1(3), 190–206 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  27. Greensmith, J., Aickelin, U., Cayzer, S.: Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 153–167. Springer, Heidelberg (2005). https://doi.org/10.1007/11536444_12

    Chapter  MATH  Google Scholar 

  28. Gumaste, S.S., Kadam, A.J.: Future weather prediction using genetic algorithm and FFT for smart farming. In: 2016 International Conference on Computing Communication Control and automation (ICCUBEA), pp. 1–6. IEEE (2016)

    Google Scholar 

  29. Hakli, H., Harun, U.: A novel approach for automated land partitioning using genetic algorithm. Expert Syst. Appl. 82, 10–18 (2017)

    Article  Google Scholar 

  30. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)

    Book  Google Scholar 

  31. Hosseini, M.S.M., Banihabib, M.E.: Optimizing operation of reservoir for agricultural water supply using firefly algorithm. J. Soil Water Resour. Conserv. 3, 17 (2014)

    Google Scholar 

  32. Hussain, K., Salleh, M.N.M., Cheng, S., Shi, Y.: Metaheuristic research: a comprehensive survey. Artif. Intell. Rev., 1–43 (2018)

    Google Scholar 

  33. Ji, Y., Zhang, M., Liu, G., Liu, Z.: Positions research of agriculture vehicle navigation system based on radial basis function neural network and particle swarm optimization. In: 2010 Sixth International Conference on Natural Computation (ICNC), pp. 480–484. IEEE (2010)

    Google Scholar 

  34. Kendall, G.: AI methods - simulated annealing (2012). http://syllabus.cs.manchester.ac.uk/pgt/2017/COMP60342/lab3/Kendall-simulatedannealing.pdf. Accessed 19 Mar 2019

  35. Kennedy, J.: The particle swarm: social adaptation of knowledge. In: IEEE International Conference on Evolutionary Computation, pp. 303–308. IEEE (1997)

    Google Scholar 

  36. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  37. Krishnanand, K., Ghose, D.: Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005, pp. 84–91. IEEE (2005)

    Google Scholar 

  38. Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194(36–38), 3902–3933 (2005)

    Article  MATH  Google Scholar 

  39. Li, Y.z., Shan-shan, Y.: Application of SVM optimized by genetic algorithm in forecasting and management of water consumption used in agriculture. In: 2010 the 2nd International Conference on Computer and Automation Engineering (ICCAE). vol. 1, pp. 625–628. IEEE (2010)

    Google Scholar 

  40. Lin, Y.P., Chang, T.K., Teng, T.P.: Characterization of soil lead by comparing sequential gaussian simulation, simulated annealing simulation and kriging methods. Environ. Geol. 41(1–2), 189–199 (2001)

    Article  Google Scholar 

  41. Lu, S., Cai, Z.j., Zhang, X.b.: Forecasting agriculture water consumption based on PSO and SVM. In: 2009 2nd IEEE International Conference on Computer Science and Information Technology (ICCSIT), pp. 147–150. IEEE (2009)

    Google Scholar 

  42. Mallawaarachchi, V.: Introduction to genetic algorithms - including example code (2017). http://www.towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3. Accessed 27 Mar 2019

  43. Mandal, S.N., Ghosh, A., Choudhury, J.P., Chaudhuri, S.B.: Prediction of productivity of mustard plant at maturity using harmony search. In: 2012 1st International Conference on Recent Advances in Information Technology (RAIT), pp. 933–938. IEEE (2012)

    Google Scholar 

  44. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  45. Nanda, S.J., Panda, G.: A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol. Comput. 16, 1–18 (2014)

    Article  Google Scholar 

  46. Nguyen, D.C.H., Ascough II, J.C., Maier, H.R., Dandy, G.C., Andales, A.A.: Optimization of irrigation scheduling using ant colony algorithms and an advanced cropping system model. Environ. Model. Softw. 97, 32–45 (2017)

    Article  Google Scholar 

  47. Noguchi, N., Terao, H.: Path planning of an agricultural mobile robot by neural network and genetic algorithm. Comput. Electron. Agric. 18(2–3), 187–204 (1997)

    Article  Google Scholar 

  48. de Ocampo, A.L.P., Dadios, E.P.: Energy cost optimization in irrigation system of smart farm by using genetic algorithm. In: 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1–7 (2017)

    Google Scholar 

  49. Oliveira, P.M., Cunha, J., Pires, E.: Evolutionary and bio-inspired algorithms in greenhouse control: introduction, review and trends. In: Intelligent Environments (2017)

    Google Scholar 

  50. Orta, A.R., Fausto, F.A.: AISearch (2018). https://aisearch.github.io/. Accessed 16 Mar 2019

  51. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)

    Article  MathSciNet  Google Scholar 

  52. Pérez-Sánchez, M., Sánchez-Romero, F.J., López-Jiménez, P.A., Ramos, H.M.: Pats selection towards sustainability in irrigation networks: simulated annealing as a water management tool. Renew. Energy 116, 234–249 (2018)

    Article  Google Scholar 

  53. Pham, D., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm technical note, pp. 1–57. Manufacturing Engineering Centre, Cardiff University, UK (2005)

    Google Scholar 

  54. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series, 1st edn. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-31306-0

    Book  MATH  Google Scholar 

  55. Rabanal, P., Rodríguez, I., Rubio, F.: Using river formation dynamics to design heuristic algorithms. In: Akl, S.G., Calude, C.S., Dinneen, M.J., Rozenberg, G., Wareham, H.T. (eds.) UC 2007. LNCS, vol. 4618, pp. 163–177. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73554-0_16

    Chapter  Google Scholar 

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

    Article  MATH  Google Scholar 

  57. Rodrigues, N.M.C.: Projeto de controladores PID com meta-heurísticas de inspiração natural e biológica. Master’s thesis, University of Trás-os-Montes e Alto Douro (2017)

    Google Scholar 

  58. Rooy, N.A.: Differential evolution optimization from scratch with Python (2017). https://nathanrooy.github.io/posts/2017-08-27/simple-differential-evolution-with-python/. Accessed 19 Mar 2019

  59. Senthilnath, J., Kulkarni, S., Benediktsson, J.A., Yang, X.S.: A novel approach for multispectral satellite image classification based on the bat algorithm. IEEE Geosci. Remote Sens. Lett. 13(4), 599–603 (2016)

    Article  Google Scholar 

  60. Sethanan, K., Neungmatcha, W.: Multi-objective particle swarm optimization for mechanical harvester route planning of sugarcane field operations. Eur. J. Oper. Res. 252(3), 969–984 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  61. Shah-Hosseini, H.: Intelligent water drops algorithm: a new optimization method for solving the multiple knapsack problem. Int. J. Intell. Comput. Cybern. 1(2), 193–212 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  62. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Technical report TR-95-012, International Computer Science Institute (1995)

    Google Scholar 

  63. Tamura, K., Yasuda, K.: Primary study of spiral dynamics inspired optimization. IEEJ Trans. Electr. Electron. Eng. 6(S1), S98 (2011)

    Article  Google Scholar 

  64. Valente, J., Del Cerro, J., Barrientos, A., Sanz, D.: Aerial coverage optimization in precision agriculture management: a musical harmony inspired approach. Comput. Electron. Agric. 99, 153–159 (2013)

    Article  Google Scholar 

  65. Van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. In: Simulated Annealing: Theory and Applications, vol. 37, pp. 7–15. Springer, Dordrecht (1987). https://doi.org/10.1007/978-94-015-7744-1_2

    Chapter  MATH  Google Scholar 

  66. Wang, H., Wang, W., Cui, Z., Zhou, X., Zhao, J., Li, Y.: A new dynamic firefly algorithm for demand estimation of water resources. Inf. Sci. 438, 95 (2018)

    Article  MathSciNet  Google Scholar 

  67. Xing, B., Gao, W.J.: Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms. ISRL, vol. 62, 1st edn. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-03404-1

    Book  MATH  Google Scholar 

  68. Yang, X.S.: Nature-Inspired Metaheuristic and Algorithms, pp. 242–246. Luniver Press, Beckington (2008)

    Google Scholar 

  69. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04944-6_14

    Chapter  Google Scholar 

  70. Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Hoboken (2010)

    Book  Google Scholar 

  71. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), vol. 284, pp. 65–74. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12538-6_6

    Chapter  Google Scholar 

  72. Yang, X.S.: Bat algorithm (Demo), July 2012. https://www.mathworks.com/matlabcentral/fileexchange/37582-bat-algorithm-demo. Accessed 15 June 2019

  73. Yang, X.-S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (eds.) UCNC 2012. LNCS, vol. 7445, pp. 240–249. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32894-7_27

    Chapter  Google Scholar 

  74. Yang, X.S., Deb, S.: Cuckoo search via lévy flights. In: World Congress on Nature & Biologically Inspired Computing 2009, pp. 210–214. IEEE (2009)

    Google Scholar 

  75. Yang, X.S., Hossein Gandomi, A.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 464–483 (2012)

    Article  Google Scholar 

  76. Yang, X.S., Papa, J.P.: Bio-inspired Computation and Applications in Image Processing. Academic Press, Amsterdam (2016)

    Book  Google Scholar 

  77. Yang, X.S., Press, L.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, Frome (2010)

    Google Scholar 

  78. Yarpiz: Ant colony optimization (ACO), September 2015. https://www.mathworks.com/matlabcentral/fileexchange/52859-ant-colony-optimization-aco. Accessed 15 June 2019

  79. Yarpiz: Binary and real-coded genetic algorithms, September 2015. https://www.mathworks.com/matlabcentral/fileexchange/52856-binary-and-real-coded-genetic-algorithms. Accessed 15 June 2019

  80. Yarpiz: Differential evolution (DE), September 2015. https://www.mathworks.com/matlabcentral/fileexchange/52897-differential-evolution-de. Accessed 15 June 2019

  81. Yarpiz: Firefly algorithm (FA), September 2015. https://www.mathworks.com/matlabcentral/fileexchange/52900-firefly-algorithm-fa. Accessed 15 June 2019

  82. Yarpiz: Harmony Search (HS), September 2015. https://www.mathworks.com/matlabcentral/fileexchange/52864-harmony-search-hs. Accessed 15 June 2019

  83. Yarpiz: Particle swarm optimization (PSO), September 2015. https://www.mathworks.com/matlabcentral/fileexchange/52857-particle-swarm-optimization-pso. Accessed 15 June 2019

  84. Yarpiz: Simulated annealing (SA), September 2015. https://www.mathworks.com/matlabcentral/fileexchange/52896-simulated-annealing-sa. Accessed 15 June 2019

  85. Zhang, Y., Wang, S., Ji, G.: A comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical Problems in Engineering (2015)

    Google Scholar 

Download references

Acknowledgements

This work was funded by FCT (Portuguese Foundation for Science and Technology), within the framework of the project “WaterJPI/0012/2016”. The authors would like to thank the EU and FCT for funding in the frame of the collaborative international consortium Water4Ever financed under the ERA-NET Water Works 2015 cofounded call. This ERA-NET is an integral part of the 2016 Joint Activities developed by the Water Challenge for a changing world joint programme initiation (Water JPI). This work was developed under the Doctoral fellowship with the reference “SFRH/BD/129813/2017”, from FCT.

Author information

Authors and Affiliations

  1. INESC TEC – Institute for Systems and Computer Engineering, Technology and Science, Pólo da UTAD, Vila Real, Portugal

    Jorge Miguel Mendes, Paulo Moura Oliveira, Filipe Neves dos Santos & Raul Morais dos Santos

  2. Engineering Department, UTAD – University of Trás-os-Montes e Alto Douro, Quinta de Prados, Vila Real, Portugal

    Jorge Miguel Mendes, Paulo Moura Oliveira & Raul Morais dos Santos

Authors
  1. Jorge Miguel Mendes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paulo Moura Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Filipe Neves dos Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Raul Morais dos Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jorge Miguel Mendes .

Editor information

Editors and Affiliations

  1. INESC-TEC, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Paulo Moura Oliveira

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. LIACC/UP, University of Porto, Porto, Portugal

    Luís Paulo Reis

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mendes, J.M., Oliveira, P.M., dos Santos, F.N., Morais dos Santos, R. (2019). Nature Inspired Metaheuristics and Their Applications in Agriculture: A Short Review. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30241-2_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30240-5

  • Online ISBN: 978-3-030-30241-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation