Evolutionary population dynamics and grey wolf optimizer | Neural Computing and Applications Skip to main content
Springer Nature Link
Log in

Evolutionary population dynamics and grey wolf optimizer

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Evolutionary population dynamics (EPD) deal with the removal of poor individuals in nature. It has been proven that this operator is able to improve the median fitness of the whole population, a very effective and cheap method for improving the performance of meta-heuristics. This paper proposes the use of EPD in the grey wolf optimizer (GWO). In fact, EPD removes the poor search agents of GWO and repositions them around alpha, beta, or delta wolves to enhance exploitation. The GWO is also required to randomly reinitialize its worst search agents around the search space by EPD to promote exploration. The proposed GWO–EPD algorithm is benchmarked on six unimodal and seven multi-modal test functions. The results are compared to the original GWO algorithm for verification. It is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm in terms of exploration, local optima avoidance, exploitation, local search, and convergence rate.

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
¥17,985 /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 includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

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

References

  1. Webster B, Bernhard PJ (2003) A local search optimization algorithm based on natural principles of gravitation. In: Proceedings of the 2003 international conference on information and knowledge engineering (IKE’03), Las Vegas, NV, USA, 2003, pp 255–261

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  4. Formato RA (2007) Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog Electromagn Res 77:425–491

    Article  Google Scholar 

  5. Alatas B (2011) ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst Appl 38:13170–13180

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  7. Du H, Wu X, Zhuang J (2006) Small-world optimization algorithm for function optimization. In: Advances in natural computation. Springer, pp 264–273

  8. Shah-Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6:132–140

    Article  Google Scholar 

  9. Moghaddam FF, Moghaddam RF, Cheriet M (2012) Curved space optimization: a random search based on general relativity theory. arXiv preprint arXiv:1208.2214

  10. Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12:702–713

    Article  Google Scholar 

  11. Abbass HA (2001) MBO: marriage in honey bees optimization—a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 congress on evolutionary computation, 2001, pp 207–214

  12. Li X (2003) A new intelligent optimization-artificial fish swarm algorithm. Doctor thesis, Zhejiang University of Zhejiang, China

  13. Roth M (2005) Termite: a swarm intelligent routing algorithm for mobile wireless ad-hoc networks. Ph. D thesis, Cornel University

  14. Pinto PC, Runkler TA, Sousa JM (2007) Wasp swarm algorithm for dynamic MAX-SAT problems. In: Adaptive and natural computing algorithms. Springer, pp 350–357

  15. Mucherino A, Seref O (2007) Monkey search: a novel metaheuristic search for global optimization. In: AIP conference proceedings, p 162

  16. Lu X, Zhou Y (2008) A novel global convergence algorithm: bee collecting pollen algorithm. In: Advanced intelligent computing theories and applications. With aspects of artificial intelligence. Springer, pp 518–525

  17. Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on nature and biologically inspired computing, 2009. NaBIC 2009, pp 210–214

  18. Shiqin Y, Jianjun J, Guangxing Y (2009) A dolphin partner optimization. In: WRI global congress on intelligent systems, 2009. GCIS’09, pp 124–128

  19. Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bioinspired Comput 2:78–84

    Article  Google Scholar 

  20. Askarzadeh A, Rezazadeh A (2013) A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer. Int J Energy Res 37(10):1196–1204

    Article  Google Scholar 

  21. Pan W-T (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74

    Article  Google Scholar 

  22. Abdel-Kader RF (2011) Hybrid discrete PSO with GA operators for efficient QoS-multicast routing. Ain Shams Eng J 2:21–31

    Article  Google Scholar 

  23. Kao Y-T, Zahara E (2008) A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl Soft Comput 8:849–857

    Article  Google Scholar 

  24. Mirjalili S, Hashim SZM (2010) A new hybrid PSOGSA algorithm for function optimization. In: 2010 international conference on computer and information application (ICCIA), 2010, pp 374–377

  25. Khamsawang S, Wannakarn P, Jiriwibhakorn S (2010) Hybrid PSO-DE for solving the economic dispatch problem with generator constraints. In: 2010 the 2nd international conference on computer and automation engineering (ICCAE), 2010, pp 135–139

  26. Shuang B, Chen J, Li Z (2011) Study on hybrid PS-ACO algorithm. Appl Intell 34:64–73

    Article  Google Scholar 

  27. El-Abd M (2011) A hybrid ABC-SPSO algorithm for continuous function optimization. In: 2011 IEEE symposium on swarm intelligence (SIS), 2011, pp 1–6

  28. Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217:3166–3173

    Article  MATH  MathSciNet  Google Scholar 

  29. Coelho LdS (2008) A quantum particle swarm optimizer with chaotic mutation operator. Chaos Solitons Fractals 37:1409–1418

    Article  Google Scholar 

  30. Lee Z-J, Su S-F, Chuang C-C, Liu K-H (2008) Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment. Appl Soft Comput 8:55–78

    Article  Google Scholar 

  31. Duan H, Yu Y, Zhang X, Shao S (2010) Three-dimension path planning for UCAV using hybrid meta-heuristic ACO–DE algorithm. Simul Model Pract Theory 18:1104–1115

    Article  Google Scholar 

  32. Lewis A, Mostaghim S, Randall (2008) Evolutionary population dynamics and multi-objective optimisation problems. In: Multi-objective optimization in computational intelligence: theory and practice, pp 185–206

  33. Bak P, Tang C, Wiesenfeld K (1987) Self-organized criticality: an explanation of the 1/f noise. Phys Rev Lett 59:381

    Article  MathSciNet  Google Scholar 

  34. Bak P (1997) How nature works. Oxford University Press, Oxford

    Google Scholar 

  35. Boettcher S, Percus AG (1999) Extremal optimization: methods derived from co-evolution. arXiv preprint arXiv:math/9904056

  36. Lewis A, Abramson D, Peachey T (2004) An evolutionary programming algorithm for automatic engineering design. In: Parallel processing and applied mathematics. Springer, pp 586–594

  37. Randall M, Lewis A (2006) An extended extremal optimisation model for parallel architectures. In: Second IEEE international conference on e-science and grid computing, 2006. e-Science’06, pp 114–114

  38. Fogel LJ (1962) Autonomous automata. Ind Res 4:14–19

    Google Scholar 

  39. Xie D, Luo Z, Yu F (2009) The computing of the optimal power consumption for semi-track air-cushion vehicle using hybrid generalized extremal optimization. Appl Math Model 33:2831–2844

    Article  MATH  Google Scholar 

  40. Randall M (2007) Enhancements to extremal optimisation for generalised assignment. In: Progress in artificial life. Springer, pp 369–380

  41. Randall M, Hendtlass T, Lewis A (2009) Extremal optimisation for assignment type problems. In: Biologically-inspired optimisation methods. Springer, pp 139–164

  42. Gómez-Meneses P, Randall M, Lewis A (2010) A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems. In: 2010 IEEE congress on evolutionary computation (CEC), 2010, pp 1–8

  43. Tamura K, Kitakami H, Nakada A (2013) Distributed modified extremal optimization using island model for reducing crossovers in reconciliation graph. Eng Lett 21:81–88

    Google Scholar 

  44. Gomez Meneses PS (2012) Extremal optimisation applied to constrained combinatorial multi-objective optimisation problems. Ph. D thesis, Bond University

  45. Tamura K, Kitakami H, Nakada A (2014) Island-model-based distributed modified extremal optimization for reducing crossovers in reconciliation graph. In: Transactions on engineering technologies. Springer, pp 141–156

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

    Article  Google Scholar 

  47. Mirjalili S, Lewis A (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput Appl 25(7–8):1569–1584

    Article  Google Scholar 

  48. Mirjalili S, Lewis A, Sadiq AS (2014) Autonomous particles groups for particle swarm optimization. Arab J Sci Eng 39(6):4683–4697

    Article  Google Scholar 

  49. Mirjalili S, Mirjalili S, Yang X-S (2014) Binary bat algorithm. Neural Comput Appl 25:663–681

    Article  Google Scholar 

  50. Mirjalili S, Wang G-G, Coelho LdS (2014) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423–1435

    Article  Google Scholar 

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

    Article  Google Scholar 

  52. Digalakis J, Margaritis K (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77:481–506

    Article  MATH  MathSciNet  Google Scholar 

  53. Molga M, Smutnicki C (2005) Test functions for optimization needs. In: Test functions for optimization needs

  54. Yang X-S (2010) Test problems in optimization. arXiv preprint arXiv:1008.0549

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

    Article  Google Scholar 

  56. Saremi S, Mirjalili S, Lewis A (2014) How important is a transfer function in discrete heuristic algorithms. Neural Comput Appl 1–16. doi:10.1007/s00521-014-1743-5

  57. Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25(5):1077–1097

    Article  Google Scholar 

  58. Saremi S, Mirjalili SM, Mirjalili S (2014) Chaotic krill herd optimization algorithm. Proc Technol 12:180–185

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Communication Technology, Nathan Campus, Griffith University, Brisbane, QLD 4111, Australia

    Shahrzad Saremi

  2. Zharfa Pajohesh System (ZPS) Co., Unit 5, NO. 30, West 208 St., Third Sq. Tehranpars, P.O. Box 1653745696, Tehran, Iran

    Seyedeh Zahra Mirjalili & Seyed Mohammad Mirjalili

  3. Queensland Institute of Business and Technology, Mt Gravatt, Brisbane, QLD 4122, Australia

    Shahrzad Saremi

Authors
  1. Shahrzad Saremi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyedeh Zahra Mirjalili
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seyed Mohammad Mirjalili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shahrzad Saremi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saremi, S., Mirjalili, S.Z. & Mirjalili, S.M. Evolutionary population dynamics and grey wolf optimizer. Neural Comput & Applic 26, 1257–1263 (2015). https://doi.org/10.1007/s00521-014-1806-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-014-1806-7

Keywords

Search

Navigation