Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

A four-step decision-making grey wolf optimization algorithm

  • Optimization
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Grey wolf optimization (GWO) is a meta-heuristic inspired by the social hierarchy and the hunting behaviors observed in wolves. The GWO algorithm has been successfully applied in many fields, such as finance, engineering and industry. However, the GWO has the disadvantages that it is prone to stagnate in local solutions, and the convergence may be slower. In order to overcome these shortcomings, we re-analyzed the hunting behaviors of wolves and observed that there are very frequent communications between leaders and ω-type wolves during the hunting process. This process is called judging prey. Based on this observation, we propose an improved optimization algorithm, termed decision-making grey wolf optimization algorithm (DGWO). At variance with the original GWO, DGWO includes four steps, instead of three: searching for prey, judging prey, encircling prey and attacking prey. The algorithm is tested using 29 well-known benchmark functions and traveling salesman problem, and compared with the GWO algorithm, as well as to the existing improved algorithms random walk grey wolf optimization and modified grey wolf optimization. Results show that including the judging prey stage in DGWO results in a faster convergence and it effectively prevents the algorithm to stop in local optima.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

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

  • Digalakis JG, Margaritis KG (2001) On benchmarking functions for genetic algorithms. Int Comput Math 77(4):481–506

    Article  MathSciNet  MATH  Google Scholar 

  • Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Article  Google Scholar 

  • Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37(2):106–111

    Article  Google Scholar 

  • Feng Z, Fei D, Wang W (2019) Face recognition by support vector machine optimized by an improved grey wolf algorithm. Comput Eng Sci 41(06):1057–1063

    Google Scholar 

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

    Article  Google Scholar 

  • Glover F, Laguna M (1998) Tabu search. Handbook of combinatorial optimization. Springer, Boston, pp 2093–2229

    Chapter  Google Scholar 

  • Gupta S, Deep K (2019a) A novel random walk grey wolf optimizer. Swarm Evol Comput 44:101–112

    Article  Google Scholar 

  • Gupta S, Deep K (2019b) Enhanced leadership-inspired grey wolf optimizer for global optimization problems. Eng Comput 36:1777–1800

    Article  Google Scholar 

  • Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Heidari AA, Pahlavani P (2017) An efficient modified grey wolf optimizer with Lévy flight for optimization tasks. Appl Soft Comput 60:115–134

    Article  Google Scholar 

  • Jayabarathi T, Raghunathan T, Adarsh BR et al (2016) Economic dispatch using hybrid grey wolf optimizer. Energy 111:630–641

    Article  Google Scholar 

  • Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  • Kaveh A, Zaerreza A (2020) Shuffled shepherd optimization method: a new meta-heuristic algorithm. Eng Comput 37:2357–2389

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kaveh A, Zakian P (2018) Improved GWO algorithm for optimal design of truss structures. Eng Comput 34(4):685–707

    Article  Google Scholar 

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

  • Kohli M, Arora S (2018) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Des Eng 5(4):458–472

    Google Scholar 

  • Koza JR, Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT Press, Cambridge

    MATH  Google Scholar 

  • Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: Proceedings 2005 IEEE swarm intelligence symposium, IEEE, SIS, pp 68–75

  • Long W, Cai SH, Jiao JJ, Wu TB (2019a) An improved grey wolf optimization algorithm. Acta Electron Sin 47(01):169–175

    Google Scholar 

  • Long W, Wu T, Cai S et al (2019b) A novel grey wolf optimizer algorithm with refraction learning. IEEE Access 7:57805–57819

    Article  Google Scholar 

  • Luo K (2019) Enhanced grey wolf optimizer with a model for dynamically estimating the location of the prey. Appl Soft Comput 77:225–235

    Article  Google Scholar 

  • Mech LD, Smith DW, MacNulty DR (2015) Wolves on the hunt: the behavior of wolves hunting wild prey. University of Chicago Press

    Book  Google Scholar 

  • Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evol Comput 9:1–14

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Yang XS (2014b) Binary bat algorithm. Neural Comput Appl 25(3–4):663–681

    Article  Google Scholar 

  • Mittal N, Singh U, Sohi BS (2016) Modified grey wolf optimizer for global engineering optimization. Appl Comput Intell Soft Comput 2016:8

    Google Scholar 

  • Molga M, Smutnicki C (2005) Test functions for optimization needs. Test functions for optimization needs, p 101

  • Muangkote N, Sunat K, Chiewchanwattana S (2014) An improved grey wolf optimizer for training q-Gaussian radial basis functional-link nets. In: 2014 international computer science and engineering conference (ICSEC). IEEE, pp 209–214

  • Nematollahi AF, Rahiminejad A, Vahidi B (2020) A novel meta-heuristic optimization method based on golden ratio in nature. Soft Comput 24(2):1117–1151

    Article  Google Scholar 

  • Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer Science & Business Media, Berlin

    MATH  Google Scholar 

  • Qais MH, Hasanien HM, Alghuwainem S (2020) Transient search optimization: a new meta-heuristic optimization algorithm. Appl Intell 50(11):3926–3941

    Article  Google Scholar 

  • Qu C, Gai W, Zhong M et al (2020) A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning. Appl Soft Comput 89:106099

    Article  Google Scholar 

  • Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  • Rashid TA, Abbas DK, Turel YK (2019) A multi hidden recurrent neural network with a modified grey wolf optimizer. PLoS ONE 14(3):e0213237

    Article  Google Scholar 

  • Saxena A, Kumar R, Das S (2019) β-Chaotic map enabled grey wolf optimizer. Appl Soft Comput 75:84–105

    Article  Google Scholar 

  • Şenel FA, Gökçe F, Yüksel AS et al (2019) A novel hybrid PSO–GWO algorithm for optimization problems. Eng Comput 35(4):1359–1373

    Article  Google Scholar 

  • Seyyedabbasi A, Kiani F (2019) I-GWO and Ex-GWO: improved algorithms of the grey wolf optimizer to solve global optimization problems. Eng Comput 37:509–532

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Song P, Liu Y (2019) Sky-wave radar location model based on improved grey wolf optimization algorithm. Laser Optoelectron Prog 56(03):234–242

    Google Scholar 

  • Sun BW, Wei SY (2019) DV-hop localization algorithm based on grey wolf optimization algorithm with adaptive adjutment strategy. Comput Sci 46(05):77–82

    Google Scholar 

  • Tan FM, Zhao JJ, Wang Q (2019) A grey wolf optimization algorithm with improved nonlinear convergence. Microelectron Comput 36(5):89–95

    Google Scholar 

  • Tawhid MA, Ali AF (2017) A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function. Memet Comput 9(4):347–359

    Article  Google Scholar 

  • Teng Z, Lv J, Guo L (2019) An improved hybrid grey wolf optimization algorithm. Soft Comput 23(15):6617–6631

    Article  Google Scholar 

  • Tu Q, Chen X, Liu X (2019) Multi-strategy ensemble grey wolf optimizer and its application to feature selection. Appl Soft Comput 76:16–30

    Article  Google Scholar 

  • Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. Simulated annealing: theory and applications. Springer, Dordrecht, pp 7–15

    Chapter  MATH  Google Scholar 

  • Wang X, Zhao H, Han T et al (2019) A grey wolf optimizer using Gaussian estimation of distribution and its application in the multi-UAV multi-target urban tracking problem. Appl Soft Comput 78:240–260

    Article  Google Scholar 

  • Wang MN, Wang QP, Wang XF (2018) Improved grey wolf optimization algorithm based on iterative mapping and simplex method. J Comput Appl 38(S2):16–20

    Google Scholar 

  • Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85

    Article  Google Scholar 

  • Wilcoxon F (1992) Individual comparisons by ranking methods. Breakthroughs in statistics. Springer, New York, pp 196–202

    Chapter  Google Scholar 

  • Yan F, Xu JZ, Li FS (2019) Training multi-layer perceptrons using chaos grey wolf optimizer. J Electron Inf Technol 41(04):872–879

    Google Scholar 

  • Yan F, Xu X, Xu J (2020) Grey wolf optimizer with a novel weighted distance for global optimization. IEEE Access 8:120173–120197

    Article  Google Scholar 

  • Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  • Yang XS (2010) Test problems in optimization. arXiv preprint http://arxiv.org/abs/1008.0549

  • Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  • Zandavi SM, Chung VYY, Anaissi A (2019) Stochastic dual simplex algorithm: a novel heuristic optimization algorithm. In: IEEE transactions on cybernetics

  • Zhu HB, Zhang Y (2018) Grey wolf optimization algorithm based on differential evolution and survival of fitness strategy. J Nanjing Univ Sci Technol 42(06):678–686

    Google Scholar 

Download references

Acknowledgements

This research is supported by Liaoning BaiQianWan Talents Program, China, with Ref. 2018921080.

Author information

Authors and Affiliations

  1. Key Laboratory of Communication and Network, Dalian University, Dalian, Liaoning, China

    Chengsheng Pan, Zenghui Si, Xiuli Du & Yana Lv

Authors
  1. Chengsheng Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zenghui Si
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiuli Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yana Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiuli Du.

Ethics declarations

Conflict of interest

The authors confirm that there exists no conflict of interest in publishing this work.

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

Pan, C., Si, Z., Du, X. et al. A four-step decision-making grey wolf optimization algorithm. Soft Comput 25, 14375–14391 (2021). https://doi.org/10.1007/s00500-021-06194-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06194-2

Keywords

Search

Navigation