Skip to main content

Advertisement

SpringerLink
Log in

A genetic algorithm-based search space splitting pattern and its application in hydraulic and coastal engineering problems

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

Abstract

This article reports a search space splitting pattern that can be applied to genetic algorithms in order to ensure that the entire search space is investigated. Hence, by keeping the genetic algorithm simple, in a reasonable time and with a high degree of accuracy, the initial solutions can be improved toward the global optimum point. The simplicity of the presented method is an advantage that makes it useful for applied hydraulic and coastal engineering problems. The performance of the proposed method was evaluated by a benchmark optimization problem, Levy No. 5, and three hydraulic and coastal engineering problems: inverse problem of Manning’s equation, the equation of equilibrium beach profiles, and the settling velocity equation of natural sediment particles. The results indicated that the nonlinear complex problems can be solved by the proposed method with a high degree of accuracy. The proposed genetic algorithm-based search space splitting pattern can either be used exclusively or alternatively it can be combined with improved operators in the literature.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Are F, Reimnitz E (2008) The A and m coefficients in the Bruun/Dean equilibrium profile equation seen from the Arctic. J Coast Res 24(sp2):243–249

    Article  Google Scholar 

  2. Bodge KR (1992) Representing equilibrium beach profiles with an exponential expression. J Coast Res 8(1):47–55

    Google Scholar 

  3. Bosurgi G, Pellegrino O, Sollazzo G (2014) Using genetic algorithms for optimizing the PPC in the highway horizontal alignment design. J Comput Civil Eng. doi:10.1061/(ASCE)CP.1943-5487.0000452,04014114

    Article  Google Scholar 

  4. Camenen B (2007) Simple and general formula for the settling velocity of particles. J Hydraul Eng 133(2):229–233. doi:10.1061/(ASCE)0733-9429

    Article  Google Scholar 

  5. Cheng MY, Prayogo D, Wu YW (2013) Novel genetic algorithm-based evolutionary support vector machine for optimizing high-performance concrete mixture. J Comput Civil Eng. doi:10.1061/(ASCE)CP.1943-5487.0000347,06014003

    Article  Google Scholar 

  6. Cheng NS (1997) Simplified settling velocity formula for sediment particle. J Hydraul Eng. doi:10.1061/(ASCE)0733-9429(1997)123:2(149),149-152

    Article  Google Scholar 

  7. Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16(3):193–203

    Article  Google Scholar 

  8. Coley DA (1999) An introduction to genetic algorithms for scientists and engineers. World Scientific, Singapore

    Book  Google Scholar 

  9. Darwin C (1973) The origin of species. Dent Gordon, London

    Google Scholar 

  10. Deep K, Thakur M (2007) A new mutation operator for real coded genetic algorithms. Appl Math Comput 193(1):211–230

    MathSciNet  MATH  Google Scholar 

  11. Dean RG, Dalrymple RA (2004) Coastal processes with engineering applications. Cambridge University Press, Cambridge

    Google Scholar 

  12. Dean RG (1977) Equilibrium beach profiles: US Atlantic and Gulf coasts. Department of Civil Engineering and College of Marine Studies, University of Delaware, Newark

    Google Scholar 

  13. Dubois RN (1999) An inverse relationship between the A and m coefficients in the Bruun/Dean equilibrium profile equation. J Coast Res 15(1):186–197

    Google Scholar 

  14. Düzgün HŞ, Uşkay SO, Aksoy A (2015) Parallel hybrid genetic algorithm and GIS-based optimization for municipal solid waste collection routing. J Comput Civil Eng. doi:10.1061/(ASCE)CP.1943-5487.0000502,04015037

    Article  Google Scholar 

  15. Elsayed SM, Sarker RA, Essam DL (2014) A new genetic algorithm for solving optimization problems. Eng Appl Artif Intel 27:57–69

    Article  Google Scholar 

  16. Espinoza FP, Minsker BS (2006) Effects of local search algorithms on groundwater remediation optimization using a self-adaptive hybrid genetic algorithm. J Comput Civil Eng. doi:10.1061/(ASCE)0887-3801(2006)20:6(420),420-430

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Boston

    MATH  Google Scholar 

  19. Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley, New York

    MATH  Google Scholar 

  20. Julien PY (1995) Erosion and sedimentation. Cambridge University Press, Cambridge

    Book  Google Scholar 

  21. Kaya M (2011) The effects of two new crossover operators on genetic algorithm performance. Appl Soft Comput 11(1):881–890

    Article  Google Scholar 

  22. Korejo I, Yang S, Li C (2010) A directed mutation operator for real coded genetic algorithms. Applications of evolutionary computation. Springer, Berlin, pp 491–500

    Chapter  Google Scholar 

  23. Krishnamoorthy CS, Prasanna Venkatesh P, Sudarshan R (2002) Object-oriented framework for genetic algorithms with application to space truss optimization. J Comput Civil Eng. doi:10.1061/(ASCE)0887-3801(2002)16:1(66),66-75

    Article  Google Scholar 

  24. Lipowski A, Lipowska D (2012) Roulette-wheel selection via stochastic acceptance. Phys A 391(6):2193–2196

    Article  Google Scholar 

  25. Monti G, Quaranta G, Marano GC (2009) Genetic-algorithm-based strategies for dynamic identification of nonlinear systems with noise-corrupted response. J Comput Civil Eng. doi:10.1061/(ASCE)CP.1943-5487.0000024,173-187

    Article  Google Scholar 

  26. Nayeem MA, Rahman MK, Rahman MS (2014) Transit network design by genetic algorithm with elitism. Transp Res C Emerg Technol 46:30–45

    Article  Google Scholar 

  27. Riazi A, Türker U (2017) Equilibrium beach profiles: erosion and accretion balanced approach. Water Environ J. doi:10.1111/wej.12245

    Article  Google Scholar 

  28. Romańczyk W, Boczar-Karakiewicz B, Bona JL (2005) Extended equilibrium beach profiles. Coast Eng 52(9):727–744

    Article  Google Scholar 

  29. Pintér JD (ed) (2006) Global optimization: scientific and engineering case studies, vol 85. Springer, New York

    MATH  Google Scholar 

  30. Soulsby R (1997) Dynamics of marine sands: a manual for practical applications. Thomas Telford, London

    Google Scholar 

  31. Türker U, Kabdaşlı MS (2006) The effects of sediment characteristics and wave height on shape-parameter for representing equilibrium beach profiles. Ocean Eng 33(2):281–291

    Article  Google Scholar 

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

    Article  Google Scholar 

  33. Wang YJ, Zhang JS (2007) An efficient algorithm for large scale global optimization of continuous functions. J Comput Appl Math 206(2):1015–1026

    Article  MathSciNet  Google Scholar 

  34. Wu W, Wang SS (2006) Formulas for sediment porosity and settling velocity. J Hydraul Eng. doi:10.1061/(ASCE)0733-9429(2006)132:8(858),858-862

    Article  Google Scholar 

  35. Zanke U (1977) Berechnung der sinkgeschwindigkeiten von sedimenten. EV

  36. Zhang RJ (1989) Sediment dynamics in rivers. Water Resources Press, Beijing (in Chinese)

    Google Scholar 

Download references

Acknowledgements

Authors would like to thank the anonymous reviewers for their helpful and constructive comments that greatly contributed to improving the final version of the article.

Author information

Authors and Affiliations

  1. Civil Engineering Department, Eastern Mediterranean University, 99450, Gazimağusa, North Cyprus, Turkey

    Amin Riazi & Umut Türker

Authors
  1. Amin Riazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Umut Türker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amin Riazi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Riazi, A., Türker, U. A genetic algorithm-based search space splitting pattern and its application in hydraulic and coastal engineering problems. Neural Comput & Applic 30, 3603–3612 (2018). https://doi.org/10.1007/s00521-017-2945-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-2945-4

Keywords

Search

Navigation