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Optimization Performance Evaluation of Evolutionary Algorithms: A Design Problem

  • M. A. Jayaram
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 250)

Abstract

This paper provides a systematic comparison of four evolutionary optimization algorithms; elitism based genetic algorithm, particle swarm optimization, ant colony optimization and artificial bee colony optimization in terms of their performance with respect to population size, convergence, fitness evaluation and percentage error on an interdisciplinary problem. The case in point is optimized design of high performance concrete mix. The methodology consists of two stages. In the first stage, a huge data base of 450 mix designs garnered through standard research publications were statistically analyzed to elicit upper and lower bounds of certain range constraints and rational ratio constraints of functional parameters. In the second stage, the four algorithms were applied to find the optimized quantities of ingredients constituting the mix. The results indicated that GA was bit high on errors, the other three algorithms showed almost same percentage of error. The convergence of bee colony optimization algorithm was fast followed by particle swarm optimization.

Keywords

Evolutionary algorithms mix design high performance concrete trial mixes constraints 

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References

  1. 1.
    Alain, B., Malhotra, M.: High volume fly ash system: Concrete solution for sustainable development. ACI Materials Journal 97(1), 41–48 (2000)Google Scholar
  2. 2.
    Zitzler, E., Thailey, L., Deb, K.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar
  3. 3.
    Yeh, I.-C.: Design of high performance concrete mixture using neural networks and non-linear programming. Journal of Computing in Civil Engineering 13(1), 36–42 (1999)CrossRefGoogle Scholar
  4. 4.
    Simon, M., Snyder, K., Fransdroff, G.: Advances in Concrete Mixture Optimization. In: Concrete Durability and Repair Technology Conference, pp. 21–32. University of Dundee, UK (1999)Google Scholar
  5. 5.
    Lim, C.-H., Yoon, Y.S., Kim, J.H.: Genetic algorithms in mix proportioning of high performance concrete. Cement and Concrete Research 34(4), 9–20 (2004)Google Scholar
  6. 6.
    Maruyama, I., Noguchi, T., Kanematsu, M.: Optimization of the concrete mix proportions centered on fresh properties using genetic algorithms. Indian Concrete Journal, 567–573 (2002)Google Scholar
  7. 7.
    Jayaram, M.A.: Innovative Methods and Applications of Soft Computing in Concrete Technology: An Interdisciplinary Approach, PhD Thesis, Visvesvaraya Technological University, Belgaum, India (2008)Google Scholar
  8. 8.
    Jayaram, M.A., Nataraja, M.C., Ravikumar, C.N.: Elitist Genetic Algorithm Models: Optimization of High Performance Concrete Mixes. Materials and Manufacturing Processes 24, 225–229 (2009)CrossRefGoogle Scholar
  9. 9.
    Jayaram, M.A., Nataraja, M.C., Ravikumar, C.N.: Design of High Performance Concrete Through Particle Swarm Optimization. Journal of Intelligent Systems 19(3), 249–264 (2010)CrossRefGoogle Scholar
  10. 10.
    Hwang, K., Noguchi, T., Tomosawa, F.: Prediction Model of Compressive Strength Development of Fly Ash Concrete. Cement and Concrete Research 34, 2269–2276 (2004)CrossRefGoogle Scholar
  11. 11.
    Goldberg, D.E., Rundnick, M.: Genetic Algorithms and Variance of Fitness. Complex Systems 5(3), 265–268 (1991)zbMATHGoogle Scholar
  12. 12.
    Ahn, C.W., Ramakrishna, R.S.: Elitism Based Compact Genetic Algorithms. IEEE Trans. Evolutionary Computtaion 7(4), 367–385 (2003)CrossRefGoogle Scholar
  13. 13.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. On Neural Networks, pp. 1942–1948 (1995)Google Scholar
  14. 14.
    Shi, Y., Eberhart, R.: Parameter Selection in Particle Swarm Optimization. In: Proc. 7th Annual Conf. On Evolutionary Programming, pp. 591–600 (1998)Google Scholar
  15. 15.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant Colony System: Optimization by a Colony of Cooperating Agents. IEEE Trans. on Systems, Man and Cybernetics-Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  16. 16.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. The MIT Press, Massachusetts (2004)zbMATHGoogle Scholar
  17. 17.
    Passino, K.M., Seely, T.D., Kirk Visscher, P.K.: Swarm Cognition in Honey Bees. Behav. Ecol. Sociobiol., 401–404 (2008)Google Scholar
  18. 18.
    Karboga, D., Basturk, B.: On the Performance of Artificial Bee Colony Algorithm. Applied Soft Computing 8(3), 687–697 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. A. Jayaram
    • 1
  1. 1.Department of Master of Computer ApplicationsSiddaganga Institute of TechnologyTumkurIndia

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