Skip to main content
SpringerLink
Log in

Integrating mutation operator into grasshopper optimization algorithm for global optimization

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The major purpose of this article is to enhance the performance of GOA algorithm by integrating a new mutation operator to the standard GOA algorithm. A series of six different variants of enhanced GOA is proposed by integrating GOA with six different variants of the mutation operator. The new enhanced metaheuristic optimization method is called EGOAs. EGOA aims to address the problems of slow convergence and trapping into local optima, by achieving a good balance between exploration and exploitation, using a special mutation operator that enhances the diversity of the standard GOA, to find the best solution for global optimization problems. The implementation process for enhancing the GOA algorithm is presented and the effectiveness of the enhanced algorithm is evaluated against 60 of the optimization benchmark functions, and compared to that of the standard GOA, as well as to other metaheuristic optimization algorithms. The performance of EGOAs was compared with the other improved methods based on GOA. Experimental results show that EGOAs is clearly superior to the standard GOA algorithm, as well as to other well-known algorithms, in terms of achieving the best optimal value, convergence speed, and avoiding local minima, which makes EGOAs a promising addition to the arsenal of metaheuristic algorithms.

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

Similar content being viewed by others

References

  • Aguilera MJB, Blum C, Moreno Vega J, Perez Perez M (2019) Hybrid metaheuristics. Springer, Berlin

    Google Scholar 

  • Ahmadi MA, Shadizadeh SR (2012) Prediction of asphaltene precipitation by using hybrid genetic algorithm and particle swarm optimization and neural network. Neural Comput Appl 4:21–30

    Google Scholar 

  • Arora S, Anand P (2018) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput Appl 31:4385–4405

    Google Scholar 

  • Bhattacharyya S, Snášel V, Pan I, De D (2019) Hybrid Computational intelligence: research and applications. CRC Press, London

    Google Scholar 

  • Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11(6):4135–4151

    MATH  Google Scholar 

  • Chugh T, Sindhya K, Hakanen J, Miettinen K (2019) A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms. Soft Comput 23(9):3137–3166

    Google Scholar 

  • Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. Proc First Eur Conf Artif Life 142:134–142

    Google Scholar 

  • Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv (CSUR) 45(3):35

    MATH  Google Scholar 

  • Ding S, Zhang Y, Chen J, Weikuan J (2013) Research on using genetic algorithms to optimize Elman neural networks. Neural Comput Appl 23(2):293–297

    Google Scholar 

  • Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. Proc Sixth Int Symp Micro Mach Hum Sci 1:39–43

    Google Scholar 

  • Ewees AA, Elaziz MA, Houssein EH (2018) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:156–172

    Google Scholar 

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

    Google Scholar 

  • Ghanem WA, Jantan A (2018) Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems. Neural Comput Appl 30(1):163–181

    Google Scholar 

  • Ghanem WA, Jantan A (2019) An enhanced Bat algorithm with mutation operator for numerical optimization problems. Neural Comput Appl 31(1):617–651

    Google Scholar 

  • Ghanem WAHM, Jantan A (2018a) A cognitively inspired hybridization of artificial bee colony and dragonfly algorithms for training multi-layer perceptrons. Cogn Comput 10(6):1096–1134

    Google Scholar 

  • Ghanem WAH, Jantan A (2018b) Hybridizing Bat algorithm with modified pitch adjustment operator for numerical optimization problems. In: Modeling, simulation, and optimization. Springer, Cham, pp 57−69

  • Ghanem WAH, Jantan A (2018c) A novel hybrid artificial bee colony with monarch butterfly optimization for global optimization problems. In: Modeling, simulation, and optimization. Springer, Cham, pp 27–38

  • Harrison KR, Engelbrecht AP, Ombuki-Berman BM (2016) Inertia weight control strategies for particle swarm optimization. Swarm Intell 10(4):267–305

    Google Scholar 

  • Hassanien AE, Emary E (2018) Swarm intelligence: principles, advances, and applications. CRC Press, London

    Google Scholar 

  • Kamboj VK, Bhadoria A, Bath SK (2017) Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer. Neural Comput Appl 28(8):2181–2192

    Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    MathSciNet  MATH  Google Scholar 

  • Kuri-Morales A (2017) Closed determination of the number of neurons in the hidden layer of a multi-layered perceptron network. Soft Comput 21(3):597–609

    Google Scholar 

  • Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877

    Google Scholar 

  • Li X, Yin M (2014) Self-adaptive constrained artificial bee colony for constrained numerical optimization. Neural Comput Appl 24(3–4):723–734

    Google Scholar 

  • Luo J, Chen H, Xu Y, Huang H, Zhao X (2018) An improved grasshopper optimization algorithm with application to financial stress prediction. Appl Math Model 64:654–668

    MathSciNet  MATH  Google Scholar 

  • Manjarres D, Landa-Torres I, Gil-Lopez S, Del Ser J, Bilbao MN, Salcedo-Sanz S, Geem ZW (2013) A survey on applications of the harmony search algorithm. Eng Appl Artif Intell 26(8):1818–1831

    Google Scholar 

  • Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. In International conference in swarm intelligence. Springer, Cham, pp 86–94

  • Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Google Scholar 

  • Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Google Scholar 

  • Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Google Scholar 

  • Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    MathSciNet  Google Scholar 

  • Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Google Scholar 

  • Mishra P, Goyal V, Shukla A (2020) An improved grasshopper optimization algorithm for solving numerical optimization problems. In: Advances in intelligent computing and communication. Springer, Singapore, pp 179–188

  • Pellerin R, Perrier N, Berthaut F (2019) A survey of hybrid metaheuristics for the resource-constrained project scheduling problem. Eur J Oper Res 280:395–416

    MathSciNet  MATH  Google Scholar 

  • Pham DT, Castellani M (2014) Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms. Soft Comput 18(5):871–903

    Google Scholar 

  • Salama KM, Abdelbar AM (2017) Learning cluster-based classification systems with ant colony optimization algorithms. Swarm Intell 11(3–4):211–242

    Google Scholar 

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

    Google Scholar 

  • Sayed GI, Hassanien AE (2017) Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images. Appl Intell 47(2):397–408

    Google Scholar 

  • Sayed GI, Khoriba G, Haggag MH (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell 48(10):3462–3481

    Google Scholar 

  • Socha K, Blum C (2007) An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput Appl 16(3):235–247

    Google Scholar 

  • Soltani P, Hadavandi E (2018) A monarch butterfly optimization-based neural network simulator for prediction of siro-spun yarn tenacity. Soft Comput. https://doi.org/10.1007/s00500-018-3624-9

    Article  Google Scholar 

  • Suresh V, Sreejith S (2017) Generation dispatch of combined solar thermal systems using dragonfly algorithm. Computing 99(1):59–80

    MathSciNet  MATH  Google Scholar 

  • Taghiyeh S, Xu J (2016) A new particle swarm optimization algorithm for noisy optimization problems. Swarm Intell 10(3):161–192

    Google Scholar 

  • Tawhid MA, Savsani V (2019) Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems. Neural Comput Appl 31(2):915–929

    Google Scholar 

  • Wang GG, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Appl 17:91–98

    Google Scholar 

  • Wang GG, Gandomi AH, Zhao X, Chu HCE (2016) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20(1):273–285

    Google Scholar 

  • Wei J, Yu Y (2019) A novel cuckoo search algorithm under adaptive parameter control for global numerical optimization. Soft Comput 24:4917–4940

    Google Scholar 

  • Wenhan X, Yuanxing W, Di Q, Daneshvar Rouyendegh B (2019) Improved grasshopper optimization algorithm to solve energy consuming reduction of chiller loading. Recovery, Utilization, and Environmental Effects Energy Sources, Part A. https://doi.org/10.1080/15567036.2019.1687622

    Article  Google Scholar 

  • Wu D, Xu S, Kong F (2016) Convergence analysis and improvement of the chicken swarm optimization algorithm. IEEE Access 4:9400–9412

    Google Scholar 

  • Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, London

    Google Scholar 

  • Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, pp 65–74

  • Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World Congress on nature and biologically inspired computing (NaBIC). IEEE, pp 210–214

  • Yang XS, Deb S, Zhao YX, Fong S, He X (2018) Swarm intelligence: past, present and future. Soft Comput 22(18):5923–5933

    Google Scholar 

  • Zhao R, Ni H, Feng H, Song Y, Zhu X (2019) An improved grasshopper optimization algorithm for task scheduling problems. Int J Innov Comput Inform Control 15(5):1967–1987

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, Malaysia

    Sanaa A. A. Ghaleb, Mumtazimah Mohamad & Engku Fadzli Hasan Syed Abdullah

  2. Faculty of Ocean Engineering Technology and Informatics, Terengganu, Malaysia

    Waheed A. H. M. Ghanem

  3. Faculty of Engineering, University of Aden, Aden, Yemen

    Sanaa A. A. Ghaleb & Waheed A. H. M. Ghanem

  4. Faculty of Education, University of Aden, Aden, Yemen

    Sanaa A. A. Ghaleb & Waheed A. H. M. Ghanem

  5. Faculty of Education-Saber, University of Aden, Aden, Yemen

    Sanaa A. A. Ghaleb & Waheed A. H. M. Ghanem

Authors
  1. Sanaa A. A. Ghaleb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mumtazimah Mohamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Engku Fadzli Hasan Syed Abdullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Waheed A. H. M. Ghanem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanaa A. A. Ghaleb.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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

Ghaleb, S.A.A., Mohamad, M., Syed Abdullah, E.F.H. et al. Integrating mutation operator into grasshopper optimization algorithm for global optimization. Soft Comput 25, 8281–8324 (2021). https://doi.org/10.1007/s00500-021-05752-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-05752-y

Keywords

Search

Navigation