Optimum design of a walking tractor handlebar through many-objective optimisation

  • Mahachai, Apichit (Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University) ;
  • Bureerat, Sujin (Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University) ;
  • Pholdee, Nantiwat (Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University)
  • Received : 2017.04.12
  • Accepted : 2017.08.17
  • Published : 2017.10.25


In this work, a comparative study of multi-objective meta-heuristics (MOMHs) for optimum design of a walking tractor handlebar is conducted in order to reduce the structural mass and increase structural static and dynamic stiffness. The design problem has objective functions as maximising structural natural frequencies, minimising structural mass, bending deflection and torsional deflection with stress constraints. The problem is classified as a many-objective optimisation since there are more than three objectives. Design variables are structural shape and size. Several well established multi-objective optimisers are employed to solve the proposed many-objective optimisation problems of the walking tractor handlebar. The results are compared whereas optimum design solutions of the walking tractor handlebar are illustrated.



Supported by : Thailand Research Fund (TRF)


  1. Aittokoski, T. and Miettinen, K. (2010), "Efficient evolutionary approach to approximate the pareto-optimal set in multiobjective optimization, UPS-EMOA", Optim. Meth. Softw., 25(6), 841-858.
  2. Bandyopadhyay, S. and Mukherjee, A. (2014), "An algorithm for many-objective optimization with reduced objective computations: A study in differential evolution", IEEE Trans. Evolut. Comput., 99, 1.
  3. Bovenzi, M. (1998), "Exposure-response relationship in the hand-arm vibration syndrome", Int. Arch. Occup. Environ. Health, 71, 509-515.
  4. Bureerat, S. and Sriworamas, K. (2007), "Population-based incremental learning for multiobjective optimization", Adv. Soft Comput., 39, 223-232.
  5. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002), "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Trans. Evolut. Comput., 6(2), 182-197.
  6. Fabbri, A., Cevoli, C. and Cantalupo, G. (2017), "A method for handlebars ballast calculation in order to reduce vibrations transmissibility in walk behind tractors", J. Agricult. Eng., 48, 2.
  7. Kanyakam, S. and Bureerat, S. (2007), "Passive vibration suppression of a walking tractor handlebar structure using multiobjective PBIL", Proceedings of the IEEE Congress on Evolutionary Computation, 4162-4169.
  8. Kanyakam, S., Srisomporn, S. and Bureerat, S. (2009), "Optimal geometrical design of multiple heights pinfin heat sink using MOPBIL", Proceedings of the 23rd Conference of the Mechanical Engineering Network of Thailand, Chiang Mai, Thailand.
  9. Kaveh, A. and Rezaei, M. (2016), "Topology and geometry optimization of different types of domes using ECBO", Adv. Comput. Des., 1(1), 1-25.
  10. Kaveh, A. and Bakhshpoori, T. (2016), "An efficient multi-objective cuckoo search algorithm for design optimization", Adv. Comput. Des., 1(1), 87-103.
  11. Kaveh, A. and Bakhshpoori, T. (2016), "Truss optimization with dynamic constraints using UECBO", Adv. Comput. Des., 1(2), 119-138.
  12. Medeiros, G.F. and Kripka, M. (2016), "Modified harmony search and its application to cost minimization of RC columns", Adv. Comput. Des., 2(1), 1-13.
  13. Nuaekaew, K., Artrit, P., Pholdee, N. and Bureerat, S. (2016), "Comparative performance of multiobjective evolutionary algorithms for solving multiobjective optimal reactive power dispatch problems", Eng. Appl. Sci. Res., 43, 18-22.
  14. Pham, A.H. (2016), "Truss discrete optimal sizing of truss using adaptive directional differential evolution", Adv. Comput. Des., 1(3), 275-296.
  15. Pholdee, N. and Bureerat, S. (2013), "Hybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trusses", Informat. Sci., 223, 136-152.
  16. Pholdee, N. and Bureerat, S. (2016), "Structural health monitoring through meta-heuristics-comparative performance study", Adv. Comput. Des., 1(4), 315-327.
  17. Pholdee, N., Bureerat, S. and Yildiz, A.R. (2017), "Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame", J. Vehic. Des., 73(1-3), 20-53.
  18. Qing-Zu, S., Qiong-He, X., Zhun, Z. and Yue-Li, Z. (2007), "Dynamic modification applied to the design of the handle of a walking tractor", J. Vehic. Mech. Mobil., 17(6), 367-378.
  19. Reyes-Sierra, M. and Coello Coello, C.A. (2006), "Multi-objective particle swarm optimizers: A survey of the state-of the-art", J. Comput. Intell. Res., 2(3), 287-308.
  20. Robic, T. and Filipic, B. (2005), "DEMO: Differential evolution for multiobjective optimization", Evolut. Multi-Criter. Optim., 3410, 520-533.
  21. Sivasubramani, S. and Swarup, K.S. (2011), "Multi-objective harmony search algorithm for optimal power flow problem", Electr. Pow. Energy Syst., 33, 745-752.
  22. Tejani, G.G., Bhensdadia, V.H. and Bureerat, S. (2016), "Topology examination of three meta-heuristic algorithms for optimal design of planar steel frames", Adv. Comput. Des., 1(1), 79-86.
  23. Yildiz, A.R. and Solanki, K.N. (2012), "Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach", J. Adv. Manufact. Technol., 59(1-4), 367-376.
  24. Yildiz, B.S. (2017), "Natural frequency optimization of vehicle components using the interior search algorithm", Mater. Test., 59(5), 456-458.
  25. Yildiz, B.S. and Lekesiz, H. (2017), "Fatigue-based structural optimisation of vehicle components", J. Vehic. Des., 73(1-3), 54-62.
  26. Zitzler, E. and Thiele, L. (1998), Multiobjective Optimization Using Evolutionary Algorithms-A Comparative Case Study, Lecture Notes in Computer Science 1498: Parallel Problem Solving from Nature-PPSN V, 1498, 292-301.