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Solving artificial ant problem using two artificial bee colony programming versions

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Abstract

Artificialant problem is considered as a sub-problem of robotic path planning. In this study, it is solved using two different methods: artificial bee colony programming and a new version of it called shrinking artificial bee colony programming. The former is a novel evolutionary computation based automatic programming method based on artificial bee colony algorithm and it was previously applied to this problem by the researchers. However, in this study, more comprehensive analyses and comparison study are provided. The shrinking artificial bee colony programming was developed in this study and its basic idea is to reduce the number of food sources, periodically, instead of a constant number used in the artificial bee colony programming. First, some parameter tuning studies were carried out for the shrinking artificial bee colony programming. Then, performances of the artificial bee colony programming, shrinking artificial bee colony programming and some other evolutionary computation based automatic programming methods were compared on Santa Fe and Los Altos Hills trails. Simulation results and the comparison study show that both of the algorithms can be used to solve the artificial ant problem effectively. Furthermore, the periodically decreasing population size property added to the artificial bee colony programming improves the performance of the algorithm on the artificial ant problem. While the proposed approach shows one of the superior performances among the considered methods, the results of the artificial bee colony programming are competitive to the methods in the comparison study.

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References

  1. Achour N, Chaalal M (2011) Mobile robots path planning using genetic algorithms. In: The seventh international conference on autonomic and autonomous systems (ICAS 2011), pp 111–115

  2. Arslan S, Ozturk C (2018) Artificial bee colony programming for feature selected cancer data classification. Int J Sci Technol Res 4(7):75–84

    Google Scholar 

  3. Arslan S, Ozturk C (2019) Artificial bee colony programming descriptor for multi-class texture classification. Appl Sci 9(9):1930. https://doi.org/10.3390/app9091930

    Article  Google Scholar 

  4. Arslan S, Ozturk C (2019) Multi hive artificial bee colony programming for high dimensional symbolic regression with feature selection. Appl Soft Comput 78:515–527

    Google Scholar 

  5. Bansal JC, Sharma H, Jadon SS (2013) Artificial bee colony algorithm: a survey. Int J Adv Intell Paradig 5(1-2):123–159

    Google Scholar 

  6. Boudardara F, Gorkemli B (2018) Application of artificial bee colony programming to two trails of the artificial ant problem. In: Proceedings of the 2nd international symposium on multidisciplinary studies and innovative technologies (ISMSIT 2018), Ankara, Turkey, October 19-21, pp 1–6, DOI https://doi.org/10.1109/ISMSIT.2018.8567048, (to appear in print)

  7. Boudouaoui Y, Habbi H (2018) Scaled artificial bee colony programming. In: 2018 International conference on applied smart systems (ICASS 2018), Medea, Algeria, November 24-25, pp 1–5, DOI https://doi.org/10.1109/ICASS.2018.8651986, (to appear in print)

  8. Brameier M, Wolfgang B (2007) Linear genetic programming., 1st edn. Springer, New York

    MATH  Google Scholar 

  9. Bullinaria JA, AlYahya K (2014) Artificial bee colony training of neural networks. In: Terrazas G, Otero F, Masegosa A (eds) Nature inspired cooperative strategies for optimization (NICSO 2013). Studies in computational intelligence, vol 512. Springer, Cham, pp 191–201

  10. Chivilikhin DS, Ulyantsev VI, Shalyto AA (2013) Solving five instances of the artificial ant problem with ant colony optimization. IFAC Proceedings Volumes 46(9):1043–1048

    Google Scholar 

  11. Christensen S, Oppacher F (2007) Solving the artificial ant on the santa fe trail problem in 20,696 fitness evaluations. In: Proceedings of the 9th annual conference on genetic and evolutionary computation (GECCO 2007), London, England, July 07-11, pp 1574–1579

  12. Contreras-Cruz MA, Ayala-Ramirez V, Hernandez-Belmonte UH (2015) Mobile robot path planning using artificial bee colony and evolutionary programming. Appl Soft Comput 30:319–328

    Google Scholar 

  13. Dal Piccol Sotto LF, De Melo VV, Basgalupp MP (2017) λ-LGP: an improved version of linear genetic programming evaluated in the ant trail problem. Knowl Inf Syst 52(2):445–465

    Google Scholar 

  14. De Mingo Lopez LF, Gomez Blas N, Morales Lucas C (2020) Ant colony systems optimization applied to BNF grammars rule derivation (ACORD algorithm). Soft Comput 24(5):3141–3154

    Google Scholar 

  15. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Compex Syst 13(2):87–129

    MathSciNet  MATH  Google Scholar 

  16. Fuchs M (1999) Large populations are not always the best choice in genetic programming. In: Proceedings of the 1st annual conference on genetic and evolutionary computation (GECCO 1999), Orlando, Florida, July 13-17, pp 1033–1038

  17. Golafshani EM, Ashour A (2016) Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques. Automat Constr 64:7–19

    Google Scholar 

  18. Golafshani EM, Behnood A (2018) Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete. Appl Soft Comput 64:377–400

    Google Scholar 

  19. Gorkemli B, Karaboga D (2019) A quick semantic artificial bee colony programming (qsABCP) for symbolic regression. Inf Sci 502:346–362

    MathSciNet  Google Scholar 

  20. Guan Y, Yang L, Sheng W (2017) Population control in evolutionary algorithms: review and comparison. In: He C, Mo H, Pan L, Zhao Y (eds) Bio-inspired computing: theories and applications. BIC-TA 2017. Communications in computer and information science, vol 791. Springer, Singapore, pp 161–174

  21. Hancer E (2020) Artificial bee colony: theory, literature review, and application in image segmentation. In: Hemanth D, Kumar B, Manavalan G (eds) Recent advances on memetic algorithms and its applications in image processing. Studies in computational intelligence, vol 873. Springer, Singapore, pp 47–67

  22. Hara A, Kushida J, Takemoto R, Takahama T (2018) Artificial bee colony programming using semantic control crossover. In: 2018 IEEE international conference on systems, man, and cybernetics, pp 189–194, DOI https://doi.org/10.1109/SMC.2018.00043, (to appear in print)

  23. Jefferson D, Collins R, Cooper C, Dyer M, Flowers M, Korf R (1991) The genesys system: evolution as a theme in artificial life. Technical report CA, pp 90024–1596

  24. Karaboga D, Akay B, Ozturk C (2007) Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In: Torra V, Narukawa Y, Yoshida Y (eds) Modeling decisions for artificial intelligence. MDAI 2007. Lecture notes in computer science, vol 4617. Springer-Verlag, Berlin, pp 318–329

  25. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  26. 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

    Google Scholar 

  27. Karaboga D, Ozturk C, Karaboga N, Gorkemli B (2012) Artificial bee colony programming for symbolic regression. Inf Sci 209: 1–15

    Google Scholar 

  28. Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11(1):652–657

    Google Scholar 

  29. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-TR06 erciyes university, Engineering faculty, Computer engineering department

  30. Kaswan KS, Choudhary S, Sharma K (2015) Applications of artificial bee colony optimization technique: survey. In: 2Nd international conference on computing for sustainable global development (INDIACom). New Delhi, India, March 11-13, pp 1660–1664

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

    MATH  Google Scholar 

  32. Koza JR (1994) Genetic programming II: automatic discovery of reusable programs. MIT Press, London

    MATH  Google Scholar 

  33. Kumar A, Kumar D, Jarial SK (2017) A review on artificial bee colony algorithms and their applications to data clustering. Cybern Inf Technol 17(3):3–28

    MathSciNet  Google Scholar 

  34. Kuroda T, Iwasawa H, Awgichew T, Kita E, Matsui N, Isokawa T (2010) Application of improved grammatical evolution to santa fe trail problems. In: Peper F, Umeo H (eds) Proceedings in information and communications technology. Natural computing, vol 2. Springer, Tokyo, pp 218–225

  35. Langdon WB, Poli R (1998) Why ants are hard. In: Genetic programming 1998: proceedings of the third annual conference, July 22-25, pp 193–201

  36. Langdon WB (1997) Fitness causes bloat: simulated annealing, hill climbing and populations technical report: CSRP-97-22. School of Computer Science, The University of Birmingham

  37. Latombe JC (1991) Robot motion planning. Springer US, New York

    MATH  Google Scholar 

  38. Luke S, Balan CG, Panait L (2003) Population implosion in genetic programming. In: Genetic and evolutionary computation-GECCO 2003. Lecture notes in computer science, vol 2724. Springer, Berlin, pp 1729–1739

  39. Miller JF (2011) Cartesian genetic programming. In: Miller J (ed) Cartesian genetic programming. Natural computing series. Springer, Berlin, pp 17–34

  40. Nababan EB, Sitompul OS, Cancer Y (2018) Genetic algorithms dynamic population size with cloning in solving traveling salesman problem. Data Sci J Comput Appl Inform 2(2):87–100

    Google Scholar 

  41. Neupane A, Goodrich MA, Mercer EG (2018) GEESE: Grammatical evolution algorithm for evolution of swarm behaviors. In: GECCO’18: genetic and evolutionary computation conference, Kyoto, Japan, July 15-19, pp 999–1006. https://doi.org/10.1145/3205455.3205619

  42. Oplatkova Z, Zelinka I (2007) Santa fe trail for artificial ant with analytic programming and three evolutionary algorithms. In: First asia international conference on modelling & simulation (AMS’07), Phuket, Thailand, March 27-30, pp 334–339

  43. Oplatkova Z, Zelinka I (2009) Investigation on evolutionary synthesis of movement commands. Model Simul Eng 2009:1–12. https://doi.org/10.1155/2009/845080

    Article  Google Scholar 

  44. Phillips T, Zhang M, Xue B (2017) Genetic programming for solving common and domain-independent generic recursive problems. In: 2017 IEEE Congress on evolutionary computation (CEC), Spain, June 05-08, pp 1279–1286. https://doi.org/10.1109/CEC.2017.7969452

  45. Spichakova M (2017) Gravitationally inspired search algorithm for solving agent tasks. Balt J Mod Comput 5(1):87–106

    Google Scholar 

  46. Spirov AV (2018) Memetic algorithms in evolutionary robotics on example of virtual bots. In: IFAC-Papersonline, vol 51, pp 586–591, DOI https://doi.org/10.1016/j.ifacol.2018.11.217

  47. Teahan WJ (2018) A grammar-directed heuristic optimisation algorithm and comparisons with grammatical evolution on the artificial ant problem. In: Lewis P, Headleand C, Battle S, Ritsos P (eds) Artificial life and intelligent agents. ALIA 2016. Communications in computer and information science, vol 732. Springer, Cham, pp 73–90

  48. Tomassini M, Vanneschi L, Cuendet J, Fernandez F (2004) A new technique for dynamic size populations in genetic programming. In: Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No. 04TH8753), Portland, OR, USA, June 19-23 pp 486–493

  49. Vadakkepat P, Tan KC, Wang ML (2000) Evolutionary artificial potential fields and their application in real time robot path planning. In: Proceedings of the 2000 congress on evolutionary computation (Cat. No.00TH8512), La Jolla, CA, USA, July 16-19, pp 256–263

  50. Zamdborg L, Holloway DM, Merelo JJ, Levchenko VF, Spirov AV (2015) Forced evolution in silico by artificial transposons and their genetic operators: the ant navigation problem. Inf Sci 306:88–110

    Google Scholar 

  51. Zhou L, Qian W, Cao G (2017) An ant colony optimization algorithm for three dimensional path planning. In: 2017 International conference on security, pattern analysis, and cybernetics (SPAC 2017), Shenzhen, China, December 15-17, pp 564–568

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  1. Computer Engineering Department, Erciyes University, Kayseri, Turkey

    Fateh Boudardara & Beyza Gorkemli

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  1. Fateh Boudardara
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  2. Beyza Gorkemli
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Correspondence to Beyza Gorkemli.

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This work was supported by Research Fund of the Erciyes University. Project Number: FYL-2018-7937.

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Boudardara, F., Gorkemli, B. Solving artificial ant problem using two artificial bee colony programming versions. Appl Intell 50, 3695–3717 (2020). https://doi.org/10.1007/s10489-020-01741-0

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