Memetic Computing

, Volume 4, Issue 1, pp 73–86 | Cite as

Bacterial memetic algorithm for offline path planning of mobile robots

  • János Botzheim
  • Yuichiro Toda
  • Naoyuki Kubota
Regular Research Paper


The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. This problem belongs to the group of combinatorial optimization problems which are approached by modern optimization techniques such as evolutionary algorithms. In this paper the bacterial memetic algorithm is proposed for path planning of a mobile robot. The objective is to minimize the path length and the number of turns without colliding with an obstacle. The representation used in the paper fits well to the algorithm. Memetic algorithms combine evolutionary algorithms with local search heuristics in order to speed up the evolutionary process. The bacterial memetic algorithm applies the bacterial operators instead of the genetic algorithm’s crossover and mutation operator. One advantage of these operators is that they easily can handle individuals with different length. The method is able to generate a collision-free path for the robot even in complicated search spaces. The proposed algorithm is tested in real environment.


Bacterial memetic algorithm Path planning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aguilar J, Colmenares A (1998) Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm. Pattern Anal Appl 1(1): 52–61zbMATHCrossRefGoogle Scholar
  2. 2.
    Ashiru I, Czarnecki C (1995) Optimal motion planning for mobile robots using genetic algorithms. In: Proceedings of the 1995 international conference on industrial automation and control, pp 297–300Google Scholar
  3. 3.
    Balázs K, Botzheim J, Kóczy LT (2010) Comparative investigation of various evolutionary and memetic algorithms. In: Rudas IJ, Fodor J, Kacprzyk J (eds) Computational intelligence in engineering. Studies in computational intelligence, vol 313. Springer, Berlin, pp 129–140Google Scholar
  4. 4.
    Botzheim J, Cabrita C, Kóczy LT, Ruano AE (2005) Fuzzy rule extraction by bacterial memetic algorithms. In: Proceedings of the 11th world congress of international fuzzy systems association, IFSA 2005, Beijing, China, pp 1563–1568Google Scholar
  5. 5.
    Botzheim J, Drobics M, Kóczy LT (2004) Feature selection using bacterial optimization. In: Proceedings of the international conference on information processing and management of uncertainty in knowledge-based systems, IPMU 2004, Perugia, Italy, pp 797–804Google Scholar
  6. 6.
    Cabrita C, Botzheim J, Gedeon TD, Ruano AE, Kóczy LT, Fonseca CM (2006) Bacterial memetic algorithm for fuzzy rule base optimization. In: Proceedings of the world automation congress, WAC 2006, Budapest, HungaryGoogle Scholar
  7. 7.
    Caponio A, Cascella GL, Neri F, Salvatore N, Sumner M (2007) A fast adaptive memetic algorithm for online and offline control design of PMSM drives. IEEE Trans Syst Man Cybernet Part B Cybernet 37: 28–41CrossRefGoogle Scholar
  8. 8.
    Cavalieri S, Gaiardelli P (1998) Hybrid genetic algorithms for a multiple-objective scheduling problem. J Intell Manufact 9(4): 361–367CrossRefGoogle Scholar
  9. 9.
    Cotta C, Troya J (1998) A hybrid genetic algorithm for the 0-1 multiple knapsack problem. In: Smith G, Steele N, Albrecht R (eds) Artificial neural nets and genetic algorithms, vol 3. Springer, New York, pp 251–255Google Scholar
  10. 10.
    Das S, Chowdhury A, Abraham A (2009) A bacterial evolutionary algorithm for automatic data clustering. In: Proceedings of the eleventh conference on congress on evolutionary computation, CEC’09, pp 2403–2410Google Scholar
  11. 11.
    Drobics M, Botzheim J (2008) Optimization of fuzzy rule sets using a bacterial evolutionary algorithm. Mathw Soft Comput 15(1): 21–40zbMATHGoogle Scholar
  12. 12.
    Fischer T, Bauer K, Merz P (2009) Solving the routing and wavelength assignment problem with a multilevel distributed memetic algorithm. Memet Comput 1(2): 101–123CrossRefGoogle Scholar
  13. 13.
    Földesi P, Botzheim J (2010) Modeling of loss aversion in solving fuzzy road transport traveling salesman problem using eugenic bacterial memetic algorithm. Memet Comput 2(4): 259–271CrossRefGoogle Scholar
  14. 14.
    Fukuda T, Kubota N (2003) (Tutorial) Computational intelligence for robotic systems. In: Proceedings of the 2003 IEEE international conference on fuzzy systems, FUZZ-IEEE2003, p 1495Google Scholar
  15. 15.
    Geisler T, Manikas T (2002) Autonomous robot navigation system using a novel value encoded genetic algorithm. In: Proceedings of the IEEE midwest symposium on circuits and systems, pp 45–48Google Scholar
  16. 16.
    Haas O, Burnham K, Mills J, Reeves C, Fisher M (1996) Hybrid genetic algorithms applied to beam orientation in radiotherapy. In: Proceedings of the fourth European congress on intelligent techniques and soft computing, pp 2050–2055Google Scholar
  17. 17.
    Harris S, Ifeachor E (1998) Automatic design of frequency sampling filters by hybrid genetic algorithm techniques. IEEE Trans Signal Process 46(12): 3304–3314CrossRefGoogle Scholar
  18. 18.
    Hasan SMK, Sarker R, Essam D, Cornforth D (2009) Memetic algorithms for solving job-shop scheduling problems. Memet Comput 1(1): 69–83CrossRefGoogle Scholar
  19. 19.
    Hermanu A (2002) Genetic algorithm with modified novel value encoding technique for autonomous robot navigation. Master’s thesis, The University of Tulsa, TulsaGoogle Scholar
  20. 20.
    Holland JH (1992) Adaption in natural and artificial systems. The MIT Press, CambridgeGoogle Scholar
  21. 21.
    Hosseinzadeh A, Izadkhah H (2010) Evolutionary approach for mobile robot path planning in complex environment. Int J Comput Sci Issues 7(4): 1–9Google Scholar
  22. 22.
    Jiao L, Gong M, Wang S, Hou B, Zheng Z, Wu Q (2010) Natural and remote sensing image segmentation using memetic computing. IEEE Comput Intell Mag 5(2): 78–91CrossRefGoogle Scholar
  23. 23.
    Kubota N, Shimojima K, Fukuda T (1996) The role of virus infection in a virus-evolutionary genetic algorithm. J Appl Math Comput Sci 6(3): 415–429Google Scholar
  24. 24.
    Luh GC, Lee SW (2006) A bacterial evolutionary algorithm for the job shop scheduling problem. J Chin Inst Indus Eng 23(3): 185–191CrossRefGoogle Scholar
  25. 25.
    Merz P, Freisleben B (1999) A comparison of memetic algorithms, tabu search, and ant colonies for the quadratic assignment problem. In: Proceedings of the 1999 congress on evolutionary computation, pp 2063–2070Google Scholar
  26. 26.
    Mester G (2010) Intelligent robot motion control in unstructured environments. Acta Polytech Hung J Appl Sci 7(4): 153–165Google Scholar
  27. 27.
    Meuth RJ, Wunsch DC, Saad EW, Vian J (2010) Memetic mission management. IEEE Comput Intell Mag 5(2): 32–40CrossRefGoogle Scholar
  28. 28.
    Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical report. Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, CaliforniaGoogle Scholar
  29. 29.
    Nawa NE, Furuhashi T (1999) Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Trans Fuzzy Syst 7(5): 608–616CrossRefGoogle Scholar
  30. 30.
    Neri F, Mininno E (2010) Memetic compact differential evolution for cartesian robot control. IEEE Comput Intell Mag 5(2): 54–65CrossRefGoogle Scholar
  31. 31.
    Ong YS, Lim MH, Chen X (2010) Research frontier: memetic computation—past present and future. IEEE Comput Intell Mag 5(2): 24–31CrossRefGoogle Scholar
  32. 32.
    Ostermark R (1999) Solving irregular econometric and mathematical optimization problems with a genetic hybrid algorithm. Comput Econ 13(2): 103–115CrossRefGoogle Scholar
  33. 33.
    Reeves C (1996) Hybrid genetic algorithms for bin-packing and related problems. Ann Oper Res 63: 371–396zbMATHCrossRefGoogle Scholar
  34. 34.
    Sasaki H, Kubota N, Taniguchi K (2008) Evolutionary computation for simultaneous localization and mapping based on topological map of a mobile robot. In: Proceedings of the first international conference on intelligent robotics and applications: part I, ICIRA ’08, pp 883–891Google Scholar
  35. 35.
    Sedighi KH, Ashenayi K, Manikas TW, Wainwright RL, Tai HM (2004) Autonomous local path planning for a mobile robot using a genetic algorithm. In: Proceedings of the 2004 IEEE congress on evolutionary computation, CEC2004, pp 1338–1345Google Scholar
  36. 36.
    Shahidi N, Esmaeilzadeh H, Abdollahi M, Lucas C (2004) Memetic algorithm based path planning for a mobile robot. In: Proceedings of the international conference on computational intelligence, pp 56–59Google Scholar
  37. 37.
    Sugihara K, Smith J (1997) Genetic algorithms for adaptive motion planning of an autonomous mobile robot. In: Proceedings of the IEEE international symposium on computational intelligence in robotics and automation, pp 138–146Google Scholar
  38. 38.
    Tang J, Lim MH, Ong YS (2007) Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput J 11(1): 873–888CrossRefGoogle Scholar
  39. 39.
    Topchy A, Lebedko O, Miagkikh V (1996) Fast learning in multilayered networks by means of hybrid evolutionary and gradient algorithms. In: Proceedings of international conference on evolutionary computation and its applications, pp 390–398Google Scholar
  40. 40.
    Tu J, Yang S (2003) Genetic algorithm based path planning for a mobile robot. In: Proceedings of the 2003 IEEE international conference on robotics and automation, pp 1221–1226Google Scholar
  41. 41.
    Xiao J, Michalewicz Z, Zhang L, Trojanowski K (1997) Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans Evol Comput 1(1): 18–28CrossRefGoogle Scholar
  42. 42.
    Yang SX, Hu Y (2005) Robot path planning in unstructured environments using a knowledge-based genetic algorithm. In: Proceedings of the 16th IFAC world congressGoogle Scholar
  43. 43.
    Zhu Z, Jia S, Ji Z (2010) Towards a memetic feature selection paradigm. IEEE Comput Intell Mag 5(2): 41–53CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • János Botzheim
    • 1
    • 2
  • Yuichiro Toda
    • 2
  • Naoyuki Kubota
    • 2
  1. 1.Department of AutomationSzéchenyi István UniversityGyőrHungary
  2. 2.Graduate School of System DesignTokyo Metropolitan UniversityHino, TokyoJapan

Personalised recommendations