Advertisement

The grid-to-neighbourhood relationship in cellular GAs: from design to solving complex problems

  • 27 Accesses

Abstract

Cellular genetic algorithms (cGAs) are a class of evolutionary algorithms in which the population is structured as a grid and interactions between individuals are restricted to the neighbourhood. Like any other optimisation algorithm, the cGA’s efficiency lies in its ability to find an adequate balance between its exploratory and exploitive capabilities. The search selection pressure represents a good indicator of the state of that balance. From that point of view, it has been shown that the cGA’s grid-to-neighbourhood relationship can be used to reflect this property. Until today, not much has been done in that area of research and many questions still surround this grid-to-neighbourhood effect. This paper describes a systematic study on the effects of that ratio on the efficiency of the cGA. This is done by proposing a dynamic cGA that adapts its ratio through evolving its grid structure using some strategy. The study is conducted using a wide range of dynamic and static ratio-control policies and, for the first time, by considering both synchronous and asynchronous cGAs. As a validation problem, we have opted for a real-world complex problem in advanced cellular networks: the users’ mobility management. A wide set of differently sized and realistic instances of this problem have been used, and several comparisons have been conducted against other top-ranked solvers. The experiments showed that the ratio strategy rules the cGA’s convergence, efficiency and scalability. Its effectiveness is correlated with the ratio-adaptation policy and the replacement synchronism being used. Indeed, our proposals that are based on deterministic and dynamic strategies with an asynchronous replacement were able to outperform most of the state-of-the-art algorithms.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Notes

  1. 1.

    High Throughput Computing (HTC).

  2. 2.

    Tukey’s results: https://tinyurl.com/Tukey-HSD.

References

  1. (2008) Mobility management problem benchmark instances. http://oplink.lcc.uma.es/problems/mmp.html

  2. Al-Naqi A, Erdogan AT, Arslan T, Mathieu Y (2010) Balancing exploration and exploitation in an adaptive three-dimensional cellular genetic algorithm via a probabilistic selection operator. In: Proceedings of the NASA/ESA conference on adaptive hardware and systems. pp 258–264

  3. Al-Naqi A, Erdogan AT, Arslan T (2011) Fault tolerant three-dimensional cellular genetic algorithms with adaptive migration schemes. In: Proceedings of the NASA/ESA conference on adaptive hardware and systems. pp 352–359

  4. Alba E, Dorronsoro B (2005) The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans Evol Comput 9:126–142

  5. Alba E, Dorronsoro B (2008) Cellular genetic algorithms, 1st edn. Springer, Berlin

  6. Alba E, Troya JM (2000) Cellular evolutionary algorithms: evaluating the influence of ratio. In: Proceedings of the 6th international conference on parallel problem solving from nature, (PPSN VI). Springer, pp 29–38

  7. Almeida-Luz SM, Vega-Rodriguez MA, Gomez-Pulido JA, Sanchez-Perez JM (2011) Differential evolution for solving the mobile location management. Appl Soft Comput 11(1):410–427

  8. Bäck T (1993) Optimal mutation rates in genetic search. In: Proceedings of the 5th international conference on genetic algorithms. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 2–8

  9. Banharnsakun A (2019) Artificial bee colony algorithm for solving the knight’s tour problem. In: Vasant P, Zelinka I, Weber GW (eds) Proceedings of the international conference on intelligent computing and optimization (ICO 2018). Springer International Publishing, pp 129–138

  10. Bar-Noy A, Kessler I (1993) Tracking mobile users in wireless communications networks. IEEE Trans Inf Theory 39(6):1877–1886

  11. Berrocal-Plaza V, Vega-Rodriguez MA, Sanchez-Perez JM (2014) A strength pareto approach to solve the reporting cells planning problem. In: Proceedings of the 14th international conference on computational science and its applications, (ICCSA). Springer, vol 8584, pp 212–223

  12. Dahi ZA (2017) Optimisation problem solving in the field of cellular networks. PhD thesis, Constantine 2 University

  13. Dahi ZA, Mezioud C, Alba E (2016) A novel adaptive genetic algorithm for mobility management in cellular networks. In: Proceedings of the 11th international conference on hybrid artificial intelligent systems, (HAIS). Springer, pp 225–237

  14. Dahi ZA, Alba E, Draa A (2018) A stop-and-start adaptive cellular genetic algorithm for mobility management of GSM-LTE cellular network users. Expert Syst Appl 106:290–304

  15. De Oliveira Barros M, Dias-Neto AC (2011) Threats to validity in search-based software engineering empirical studies, pp 1–12. UNIRO

  16. Dorronsoro B, Bouvry P (2011) Adaptive neighborhoods for cellular genetic algorithms. In: Proceedings of the IEEE international symposium on parallel and distributed processing workshops and Phd forum. pp 388–394

  17. Eiben AE, Smith JE (2015) Parameters and parameter tuning. Springer, Berlin Heidelberg, pp 119–129

  18. Fahad AM, Ahmed AA, Kahar MNM (2019) Network intrusion detection framework based on whale swarm algorithm and artificial neural network in cloud computing. In: Vasant P, Zelinka I, Weber GW (eds) Proceedings of the international conference on intelligent computing and optimization (ICO 2018). Springer International Publishing, pp 56–65

  19. González-Álvarez DL, Rubio-Largo A, Vega-Rodríguez MA, Almeida-Luz SM, Gómez-Pulido JA, Sánchez-Pérez JM (2012) Solving the reporting cells problem by using a parallel team of evolutionary algorithms. Logic J IGPL 20(4):722–731

  20. Grefenstette JJ (1986) Optimization of control parameters for genetic algorithms. IEEE Trans Syst Man Cybern 16(1):122–128

  21. Hać A, Zhou X (1997) Locating strategies for personal communication networks, a novel tracking strategy. IEEE J Sel Areas Commun 15(8):1425–1436

  22. Huang A, Li D, Hou J, Bi T (2015) An adaptive cellular genetic algorithm based on selection strategy for test sheet generation. Int J Hybrid Inf Technol 8:33–42

  23. Jie L, Liu W, Sun Z, Teng S (2017) Hybrid fuzzy clustering methods based on improved self-adaptive cellular genetic algorithm and optimal-selection-based fuzzy c-means. Neurocomputing 249:140–156

  24. Kamkar I, Akbarzadeh TM (2010) Multiobjective cellular genetic algorithm with adaptive fuzzy fitness granulation. In: Proceedings of the IEEE international conference on systems, man and cybernetics. pp 4147–4153

  25. Karafotias G, Hoogendoorn M, Eiben AE (2015) Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans Evol Comput 19(2):167–187

  26. Lechuga GP, Sánchez FM (2019) Modeling and optimization of flexible manufacturing systems: A stochastic approach. In: Vasant P, Zelinka I, Weber GW (eds) Proceedings of the international conference on intelligent computing and optimization (ICO 2018). Springer International Publishing, pp 539–546

  27. Lin L, Gen M (2009) Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation. Soft Comput 13(2):157–168

  28. Malhotra R, Khanna M (2018) Threats to validity in search-based predictive modelling for software engineering. IET Softw 12(4):293–305

  29. Morales-Reyes A, Stefatos EF, Erdogan AT, Arslan T (2008) Towards fault-tolerant systems based on adaptive cellular genetic algorithms. In: Proceedings of the NASA/ESA conference on adaptive hardware and systems. pp 398–405

  30. Pang J, He J, Dong H (2018) Hybrid evolutionary programming using adaptive lévy mutation and modified nelder-mead method. Soft Comput. https://doi.org/10.1007/s00500-018-3422-4

  31. Razavi S (2011) Tracking area planning in cellular networks. PhD thesis, Department of Science and Technology, Linkoping University

  32. Sarma J, De Jong K (1996) An analysis of the effects of neighborhood size and shape on local selection algorithms. In: Proceedings of the 4th international conference on parallel problem solving from nature parallel problem solving from nature, (PPSN IV). Springer, pp 236–244

  33. Sarma J, Jong KAD (1997) An analysis of local selection algorithms in a spatially structured evolutionary algorithm. In: Proceedings of the 7th international conference on genetic algorithms. pp 181–187

  34. Schraudolph NN, Belew RK (1992) Dynamic parameter encoding for genetic algorithms. Mach Learn 9(1):9–21

  35. Sivanandam SN, Deepa SN (2007) Introduction to genetic algorithms, 1st edn. Springer, Berlin

  36. Sun CT, Wu MD (1995) Self-adaptive genetic algorithm learning in game playing. In: Proceedings of the IEEE international conference on evolutionary computation. vol 2, pp 814–818

  37. Talbi EG (2009) Metaheuristics: from design to implementation. Wiley Publishing, Hoboken

  38. Tian M, Gao X (2019) Differential evolution with neighborhood-based adaptive evolution mechanism for numerical optimization. Inf Sci 478:422–448

  39. Torres-Escobar R, Marmolejo-Saucedo JA, Litvinchev I, Vasant P (2019) Monkey algorithm for packing circles with binary variables. In: Vasant P, Zelinka I, Weber GW (eds) Proceedings of the international conference on intelligent computing and optimization (ICO 2018). Springer International Publishing, pp 547–559

  40. Zhang J, Chen WN, Zhan ZH, Yu WJ, Li YL, Chen N, Zhou Q (2012) A survey on algorithm adaptation in evolutionary computation. Front Electr Electron Eng 7(1):16–31

  41. Zhang L, Tian JH, Jiang J, Liu YJ, Pu MY, Yue T (2018) Empirical research in software engineering-a literature survey. J Comput Sci Technol 33(5):876

Download references

Author information

Correspondence to Zakaria Abdelmoiz Dahi.

Ethics declarations

Conflict of interest

The authors would like to thank Mr. Louay Rabah Dahi for his help in checking the numerical results. Also, author B acknowledges partial funding from Spanish-plus-FEDER and MINECO project moveON TIN2014-57341-R, TIN2016-81766-REDT and TIN2017-88213-R. Author A declares that he has 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.

Communicated by V. Loia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dahi, Z.A., Alba, E. The grid-to-neighbourhood relationship in cellular GAs: from design to solving complex problems. Soft Comput (2019) doi:10.1007/s00500-019-04125-w

Download citation

Keywords

  • Cellular genetic algorithms
  • Adaptation
  • Cellular networks
  • Mobility management