Genetic Algorithm Parameter Effect on 3D Truss Optimization with Discrete Variable

  • Akshay Kumar Dept. of Mechanical Engineering, BMS College of Engineering, Bull Temple Road, Bengaluru, INDIA
  • H K Rangavittal Dept. of Mechanical Engineering, BMS College of Engineering, Bull Temple Road, Bengaluru, INDIA


The Genetic Algorithm is one of the advanced optimization techniques frequently used for solving complex problems in the research field, and there are plenty of parameters which affect the outcome of the GA. In this study, a 25-bar truss with the nonlinear constraint is chosen with the objective to minimize the mass and variables being the discrete area. For the same, GA parameter like Selection Function, Population Size, Crossover Function, and Creation Function are varied to find the best combination with minimum function evaluation. It is found that the Uniform selection gives the best result irrespective of the creation function, population size or crossover functions. But this is at the cost of a large number of function evaluations, and the other selection function fails to reach the global optimum and has a smaller number of function evaluation count. If the analysis of selection function is done one at a time, it is seen that all Cases performs better in Roulette but, Case A which is non-integer type with 200 population size being computationally cheaper than Case B and C of population size 300. In the Tournament selection, Case A, B with smaller population size and Case C with higher population size performs better. Case C performs better at Remainder selection with smaller population size, and Case A and B for Stochastic Uniform with higher population size. And, it is clear that the function evaluation count increases with the population size in every Case from this study.

Keywords: Genetic Algorithm, 3D Truss, Optimization, Discrete Variable, MATLAB


Download data is not yet available.


[1]           C. Camp, S. Pezeshk, and G. Cao, “Design of framed structures using a genetic algorithm,” in Proceedings of the US-Japan Joint Seminar on Structural Optimization, 1997. ISBN:0784402213, 9780784402214

[2]           Rajeev S. and C. S. Krishnamoorthy, “Discrete optimization of structures using genetic algorithms,” J. Struct. Eng., vol. 118, no. 5, pp. 1233–1250, 1992.

[3]           V. Toǧan and A. T. Daloǧlu, “Optimization of 3d trusses with adaptive approach in genetic algorithms,” Eng. Struct., vol. 28, no. 7, pp. 1019–1027, 2006.

[4]           T. Dede, S. Bekirolu, and Y. Ayvaz, “Weight minimization of trusses with genetic algorithm,” Appl. Soft Comput. J., vol. 11, no. 2, pp. 2565–2575, 2011.

[5]           O. Hasançebi and F. Erbatur, “Evaluation of crossover techniques in genetic algorithm based optimum structural design,” Comput. Struct., vol. 78, no. 1, pp. 435–448, 2000.

[6]           C. A. Coello and A. D. Christiansen, “Multiobjective optimization of trusses using genetic algorithms,” Comput. Struct., vol. 75, no. 6, pp. 647–660, 2000.

[7]           J. F. Schutte and A. A. Groenwold, “Sizing design of truss structures using particle swarms,” Struct. Multidiscip. Optim., vol. 25, no. 4, pp. 261–269, 2003.

 [8]          M. Kripka, “Discrete optimization of trusses by simulated annealing,” J. Brazilian Soc. Mech. Sci. Eng., vol. 26, no. 2, pp. 170–173, 2004.s

[9]           A. Kaveh, and S. Talatahari, “A hybrid particle swarm and ant colony optimization for design of truss structures,” Asian J. Civ. Eng., vol. 9, no. 4, pp. 329–-348, 2008.

[10]         A. Kaveh, and S. Talatahari, “Size optimization of space trusses using Big Bang-Big Crunch algorithm,” Comput. Struct., vol. 87, no. 17–18, pp. 1129–1140, 2009.

[11]         G. C. Luh and C. Y. Lin, “Optimal design of truss-structures using particle swarm optimization,” Comput. Struct., vol. 89, no. 23–24, pp. 2221–2232, 2011.

[12]         Z. El Maskaoui, S. Jalal, and L. Bousshine, “Genetic Algorithm Parameters Effect on the Optimal Structural Design Search,” IOSR J. of Mech and Civ Eng, vol. 14, no. 3, pp. 124–130, 2017.

[13]         R. P and S. C.R, “Optimal Design of Plane Truss Structures Using Differential Evolution Algorithm,” Jordan J. Civ. Eng., vol. 11, no. 1, pp. 91–96, 2017.

[14]         D. Neeraja, T. Kamireddy, P. S. Kumar, and V. S. Reddy, “Weight optimization of plane truss using genetic algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 263, no. 3, 2017.

[15]         H. Assimi, A. Jamali, and N. Nariman-zadeh, “Sizing and topology optimization of truss structures using genetic programming,” Swarm Evol. Comput., vol. 37, pp. 90–103, 2017.

[16]         I. N. Serpik, A. V. Alekseytsev, and P. Y. Balabin, “Mixed approaches to handle limitations and execute mutation in the genetic algorithm for truss size, shape and topology optimization,” Period. Polytech. Civ. Eng., vol. 61, no. 3, pp. 471–482, 2017.

[17]         V. R. Kalatjari and M. H. Talebpour, “Optimization of skeletal structure using improved genetic algorithm based on proposed sampling search space idea,” Int. J. Optim. Civil Eng, vol. 8, no. 3, pp. 415–432, 2018.

[18]         A. Kaveh and S. Shojaee, “Optimal design of skeletal structures using ant colony optimization,” Int. J. Numer. Methods Eng., no. October 2006, pp. 1885–1891, 2006.

How to Cite
A. Kumar and H. Rangavittal, “Genetic Algorithm Parameter Effect on 3D Truss Optimization with Discrete Variable”, Adv. J. Grad. Res., vol. 5, no. 1, pp. 61-70, Dec. 2018.
Graduate Research Articles