Skip to main content

Advertisement

SpringerLink
Log in

Detection of object boundary from point cloud by using multi-population based differential evolution algorithm

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

Abstract

The problem-solving success of an Evolutionary Computing algorithm is too sensitive to the structures of mutation and crossover operators it uses. The mutation operator generates the trial vectors necessary for the relevant Evolutionary Computing method to perform efficient global and local search in the search space. Partial elitist mutation operators can produce more efficient trial vectors than isotropic mutation strategies. Another numerical genetic operator that affects the process of producing efficient trial vectors is crossover. Due to the dominant effect of the Evolutionary Computing algorithms of mutation and crossover operators on problem solving capacity, new numerical-genetic operators are constantly being developed. When solving a problem with the Differential Evolution Algorithm determining the ideal mutation operator and setting the initial values of the internal parameters of the crossover operator is quite time-consuming and difficult. In this paper, the Multi-population Based Differential Evolution Algorithm (MDE) has been proposed to solve real-valued numerical optimization problems with its convergence proof. The mutation operator of MDE is partial—elitist and its crossover operator is parameter-free, in practice. In this paper, 28 benchmark problems of CEC2013 with Dim = 20 and one real-world geometric optimization problem have been used in the experiments performed to examine the numerical problem-solving success of MDE. MDE's success in solving related benchmark problems has been statistically compared with ABC, CK, SOS and GWO. Statistical analysis of the results obtained from the experiments exposed that MDE is statistically more successful than comparison methods in solving numerical optimization problems used.

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

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article as no new data were created or analysed in this study.

References

  1. Yoon Y, Kim Y-H (2013) An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Trans Cybern 43(5):1473–1483

    Article  Google Scholar 

  2. Heidari AA, Abbaspour RA, Jordehi AR (2017) An efficient chaotic water cycle algorithm for optimization tasks. Neural Comput Appl 28(1):57–85

    Article  Google Scholar 

  3. Wang D, Wu Z, Fei Y, Zhang W (2014) Structural design employing a sequential approximation optimization approach. Comput Struct 134:75–87

    Article  Google Scholar 

  4. Wu X, Yang Z (2013) Nonlinear speech coding model based on genetic programming. Appl Soft Comput 13(7):3314–3323

    Article  Google Scholar 

  5. Yan Y, He Y, Hu Y, Guo B (2014) Video superresolution via parameter-optimized particle swarm optimization. Math Probl Eng. https://doi.org/10.1155/2014/373425

    Article  MATH  Google Scholar 

  6. Moezi SA, Zakeri E, Zare A, Nedaei M (2015) On the application of modified cuckoo optimization algorithm to the crack detection problem of cantilever Euler–Bernoulli beam. Comput Struct 157:42–50

    Article  Google Scholar 

  7. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  8. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  9. Ozturk C, Hancer E, Karaboga D (2015) A novel binary artificial bee colony algorithm based on genetic operators. Inf Sci 297:154–170

    Article  MathSciNet  Google Scholar 

  10. Collard P, Escazut C (1995) Genetic operators in a dual genetic algorithm. In: Proceedings of 7th IEEE international conference on tools with artificial intelligence, IEEE, pp 12–19

  11. Qin Q, Cheng S, Zhang Q, Wei Y, Shi Y (2015) Multiple strategies based orthogonal design particle swarm optimizer for numerical optimization. Comput Oper Res 60:91–110

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  13. Civicioglu P, Besdok E (2019) Bernstain-search differential evolution algorithm for numerical function optimization. Expert Syst Appl 138:112831

    Article  Google Scholar 

  14. Civicioglu P, Besdok E, Gunen MA, Atasever UH (2020) Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms. Neural Comput Appl 32(8):3923–3937

    Article  Google Scholar 

  15. Civicioglu P (2013) Artificial cooperative search algorithm for numerical optimization problems. Inf Sci 229:58–76

    Article  MATH  Google Scholar 

  16. Civicioglu P, Besdok E (2018) A+ Evolutionary search algorithm and QR decomposition based rotation invariant crossover operator. Expert Syst Appl 103:49–62

    Article  Google Scholar 

  17. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  18. Raut U, Mishra S (2019) An improved Elitist-Jaya algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems. Renew Energy Focus 30:92–106

    Article  Google Scholar 

  19. Carrasco J, García S, Rueda M, Das S, Herrera F (2020) Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: practical guidelines and a critical review. Swarm Evol Comput 54:100665

    Article  Google Scholar 

  20. Slowik A, Kwasnicka H (2020) Evolutionary algorithms and their applications to engineering problems. Neural Comput Appl 32:12363–12379

    Article  Google Scholar 

  21. Leite JP, Topping BH (1998) Improved genetic operators for structural engineering optimization. Adv Eng Softw 29(7–9):529–562

    Article  Google Scholar 

  22. Takahashi RH, Vasconcelos J, Ramírez JA, Krahenbuhl L (2003) A multiobjective methodology for evaluating genetic operators. IEEE Trans Magn 39(3):1321–1324

    Article  Google Scholar 

  23. Jiang D, Tian Z, He Z, Tu G, Huang R (2021) A framework for designing of genetic operators automatically based on gene expression programming and differential evolution. Nat Comput 395:1–17

    MathSciNet  Google Scholar 

  24. Katoch S, Chauhan SS, Kumar V (2021) A review on genetic algorithm: past, present, and future. Multimedia Tools Appl 80(5):8091–8126

    Article  Google Scholar 

  25. Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer, Berlin. ISBN: 978-3-642-42416-8

  26. Civicioglu P, Besdok E (2013) A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346

    Article  Google Scholar 

  27. Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces: technical report TR-95-012, International Computer Science, Berkeley, California

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

    Article  Google Scholar 

  29. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

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

  31. Civicioglu P, Besdok E (2014) Comparative analysis of the cuckoo search algorithm. In: Cuckoo search and firefly algorithm, Springer, pp 85–113. ISBN: 978-3-319-02141-6

  32. Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    Article  Google Scholar 

  33. Abdullahi M, Ngadi MA (2016) Symbiotic organism search optimization based task scheduling in cloud computing environment. Futur Gener Comput Syst 56:640–650

    Article  Google Scholar 

  34. Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30(2):413–435

    Article  Google Scholar 

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

    Article  Google Scholar 

  36. Civicioglu P, Alci M (2004) Besdok, E (2004) Impulsive noise suppression from images with the noise exclusive filter. EURASIP J Appl Signal Process 16:2434–2440

    MATH  Google Scholar 

  37. Besdok E, Civicioglu P, Alci M (2004) Impulsive noise suppression from highly corrupted images by using resilient neural networks. In: 7th International conference on artificial intelligence and soft computing—ICAISC 2004, vol 3070, pp 670–675

  38. Civicioglu P, Alci M (2004) Edge detection of highly distorted images suffering from impulsive noise. AEU Int J Electron Commun 58(6):413–419

    Article  Google Scholar 

  39. Gunen MA, Civicioglu P, Besdok E (2016) Differential search algorithm based edge detection. In: 23rd Congress of the international-society-for-photogrammetry-and-remote-sensing (ISPRS) XXIII ISPRS congress, commission VII. 41 (B7), pp 667–670

  40. Knobloch R, Mlynek J, Srb R (2017) The classic differential evolution algorithm and its convergence properties. Appl Math 62:197–208

    Article  MathSciNet  MATH  Google Scholar 

  41. Knobloch R, Mlynek J (2020) Probabilistic analysis of the convergence of the differential evolution algorithm. Neural Netw World 30(4):249–263

    Article  Google Scholar 

  42. Wang X, Chen H, Heidari AA, Zhang X, Xu J, Xu Y, Huang H (2020) Multi-population following behavior-driven fruit fly optimization: a Markov chain convergence proof and comprehensive analysis. Knowl Based Syst 210:106437

    Article  Google Scholar 

  43. Zapotecas-Martínez S, García-Nájera A, Menchaca-Méndez A (2022) Improved lebesgue indicator-based evolutionary algorithm: reducing hypervolume computations. Mathematics 10:19. https://doi.org/10.3390/math10010019

    Article  Google Scholar 

  44. https://github.com/Aekarkinli/Multi-Population-based-Differential-Evolution-Algorithm.git

  45. Kulakoğlu F, Öztürk G (2015) New evidence for international trade in Bronze Age central Anatolia: recently discovered bullae at Kültepe-Kanesh. Antiquity 89(343):261–262

    Google Scholar 

  46. Liang J, Qu B, Suganthan P, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou Univercity, Zhengzhou, China Nanyang Technological University, Singapore, Technical Report, vol 201212, no 34, pp 281–295

  47. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geomatics Engineering, Faculty of Engineering, Nigde Omer Halisdemir University, Nigde, Turkey

    Ahmet Emin Karkinli

Authors
  1. Ahmet Emin Karkinli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmet Emin Karkinli.

Ethics declarations

Conflict of interest

The author declares that there is no conflict of interest regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karkinli, A.E. Detection of object boundary from point cloud by using multi-population based differential evolution algorithm. Neural Comput & Applic 35, 5193–5206 (2023). https://doi.org/10.1007/s00521-022-07969-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07969-w

Keywords

Search

Navigation