An Improved Grasshopper Optimization Algorithm for Solving Numerical Optimization Problems

  • Puneet Mishra
  • Vishal Goyal
  • Aasheesh Shukla
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)


This paper proposes a modified and improved grasshopper optimization algorithm (IGOA) for solving complex and challenging optimization problems. Grasshopper optimization algorithm (GOA) is a recently proposed bio-inspired swarm optimization algorithm which is based on the swarm nature of grasshoppers. It mainly relies upon the social interaction forces to find global optimum values of an optimization problem. However, it has a strong tendency to move towards the current optimal and hence may get trapped in local optimal points. This behaviour is strongly related to a factor, known as c factor, in GOA which varies linearly throughout the iterations from a maximum to minimum value. This work proposes two modifications in conventional GOA to avoid premature convergence of GOA. These modifications include a novel c factor variation scheme and inclusion of random walks between grasshoppers to attain global optimum points. The performance of IGOA is tested on 19 benchmark test functions for 20 independent trial runs. For all the cases, it was observed that performance of IGOA was superior to GOA and it outperformed the GOA in terms of accuracy, speed, and repeatability for all the considered test functions.


Meta-heuristic optimization Global optimization Bio-inspired optimization techniques 


  1. 1.
    Spall JC (2005) Introduction to stochastic search and optimization: estimation, simulation, and control, vol. 65. John Wiley & SonsGoogle Scholar
  2. 2.
    Harik GR, Lobo FG, Goldberg DE (1999) The compact genetic algorithm. IEEE Trans Evol Comput 3(4):287–297CrossRefGoogle Scholar
  3. 3.
    Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73CrossRefGoogle Scholar
  4. 4.
    Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer USA, pp 760–766Google Scholar
  5. 5.
    Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRefGoogle Scholar
  6. 6.
    Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, pp 65–74CrossRefGoogle Scholar
  7. 7.
    Yang XS, Hossein Gandomi A (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483CrossRefGoogle Scholar
  8. 8.
    Yang XS (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, Heidelberg, pp 240–249CrossRefGoogle Scholar
  9. 9.
    Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237MathSciNetCrossRefGoogle Scholar
  10. 10.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  11. 11.
    Mirjalili S, Saremi S, Mirjalili SM, Coelho LDS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119CrossRefGoogle Scholar
  12. 12.
    Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Nature and biologically inspired computing, 2009. NaBIC 2009. World Congress on. IEEE, pp 210–214Google Scholar
  13. 13.
    Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343zbMATHGoogle Scholar
  14. 14.
    Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249CrossRefGoogle Scholar
  15. 15.
    Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98CrossRefGoogle Scholar
  16. 16.
    Mirjalili S, Jangir P, Saremi S (2017) Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell 46(1):79–95CrossRefGoogle Scholar
  17. 17.
    Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845MathSciNetCrossRefGoogle Scholar
  18. 18.
    Wang GG, Guo L, Gandomi AH, Hao GS, Wang H (2014) Chaotic krill herd algorithm. Inf Sci 274:17–34MathSciNetCrossRefGoogle Scholar
  19. 19.
    Dorigo M, Birattari M (2011) Ant colony optimization. In: Encyclopedia of machine learning. Springer, Boston, MA, pp 36–39Google Scholar
  20. 20.
    Afshar A, Haddad OB, Marino MA, Adams BJ (2007) Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. J Franklin Inst 344(5):452–462CrossRefGoogle Scholar
  21. 21.
    Fu Y, Ding M, Zhou C (2012) Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. IEEE Trans Syst, Man, Cybern-Part A: Syst Humans 42(2):511–526CrossRefGoogle Scholar
  22. 22.
    Mishra P, Kumar V, Rana KPS (2015) A fractional order fuzzy PID controller for binary distillation column control. Expert Syst Appl 42(22):8533–8549CrossRefGoogle Scholar
  23. 23.
    Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47CrossRefGoogle Scholar
  24. 24.
    Chattaraj N, Ganguli R (2017) Multi-objective optimization of a triple layer piezoelectric bender with a flexible extension using genetic algorithm. Mech Adv Mater Struct 1–9Google Scholar
  25. 25.
    Roberge V, Tarbouchi M, Labonté G (2013) Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans Industr Inf 9(1):132–141CrossRefGoogle Scholar
  26. 26.
    Mishra P, Kumar V, Rana KPS (2018) An efficient method for parameter estimation of a nonlinear system using backtracking search algorithm. Eng Sci Technol Int J 21(3):338–350CrossRefGoogle Scholar
  27. 27.
    Surjanovic S, Bingham D (2013) Virtual library of simulation experiments. Last Accessed on 29 May 2018

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Puneet Mishra
    • 1
  • Vishal Goyal
    • 2
  • Aasheesh Shukla
    • 2
  1. 1.Department of Electrical and Electronics EngineeringBirla Institute of Technology and Science, PilaniJhunjhunuIndia
  2. 2.Department of Electronics and Communication EngineeringGLA UniversityMathuraIndia

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