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A Hybrid Search Method Based on the Artificial Bee Colony Algorithm

인공벌 군집 알고리즘을 기반으로 한 복합탐색법

  • Lee, Su-Hang (Department of Mechanical Engineering, Hanyang Graduate School) ;
  • Kim, Il-Hyun (Department of Mechanical Engineering, Hanyang Graduate School) ;
  • Kim, Yong-Ho (Department of Mechanical Engineering, Hanyang Graduate School) ;
  • Han, Seog-Young (Division of Mechanical Engineering, Hanyang University)
  • Received : 2014.03.24
  • Accepted : 2014.05.22
  • Published : 2014.06.15

Abstract

A hybrid search method based on the artificial bee colony algorithm (ABCA) with harmony search (HS) is suggested for finding a global solution in the field of optimization. Three cases of the suggested algorithm were examined for improving the accuracy and convergence rate. The results showed that the case in which the harmony search was implemented with the onlooker phase in ABCA was the best among the three cases. Although the total computation time of the best case is a little bit longer than the original ABCA under the prescribed conditions, the global solution improved and the convergence rate was slightly faster than those of the ABCA. It is concluded that the suggested algorithm improves the accuracy and convergence rate, and it is expected that it can effectively be applied to optimization problems with many design variables and local solutions.

Keywords

References

  1. Dorigo, M., Stutzle, T., 2004, Ant Colony Optimization, MIT Press, London.
  2. Kennedy, J., Eberhart, R., 1995, Particle Swarm Optimization, Proc. 1995 IEEE Int. Conf. Neural Netw. 4 1942-1948.
  3. Yang, X-S., 2010, A New Metaheuristic Bat-inspired Algorithm, Nat. Inspir. Coop. Strateg. Optim. (NICSO 2010) Stud. Comput. Intell. 284 65-74.
  4. Karaboga, D., Basturk, B., 2008, On the Performance of Artificial Bee Colony (ABC) Algorithm, Appl. Soft Comput. 8:1 687-697. https://doi.org/10.1016/j.asoc.2007.05.007
  5. Storn, R., Price, K., 1997, Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, J. Glob. Optim. 11:4 341-359. https://doi.org/10.1023/A:1008202821328
  6. Back, T., 1996, Evolutionary Algorithms in Theory and Practice: Evolution, Strategies, Evolution Programming, Genetic Algorithms, Oxford Univ. Press, New York.
  7. Park, J.-Y., Ryu, S.-P., Eom, Y.-S., Yoo, K.-S., Park, J.-Y., Han, S.-Y., 2010, Application of Modified Bee Colony Algorithm for Structural Dynamic Problems, Korea Soc. Mach. Tool Eng. Autumn Conf. 2010 57-58.
  8. Park. J. Y., Han, S. Y., 2013, Topology optimization of nonlinear Structures Using Bee Colony Optimization, Korea Soc. Manuf. Technol. Eng. Spring Conf. 2013 193.
  9. Lee, K. S., Geem, Z. W., 2004, A New Structural Optimization Method Based on the Harmony Search Algorithm, Comput. Struct. 82:9-10 781-798. https://doi.org/10.1016/j.compstruc.2004.01.002
  10. Geem, Z. W., 2006, Comparison Harmony Search with Other Meta-heuristics in Water Distribution Network Design, Water Distrib. Syst. Anal. Symp. 2006.
  11. Kim, J. H., Geem, Z. W., Kim, E. S., 2001, Parameter Estimation of the Nonlinear Muskingum Model Using Harmony Search, J. Am. Water Resour. Assoc. 37:5 1131-1138. https://doi.org/10.1111/j.1752-1688.2001.tb03627.x
  12. Zarei, O., Fesanghary, M., Farshi, B., Jalili Saffar, R., Razfar, M. R., 2009, Optimization of Multi-pass Face-milling via Harmony Search Algorithm, J. Mater. Process. Technol. 209:5 2386-2392. https://doi.org/10.1016/j.jmatprotec.2008.05.029
  13. Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publ. Co., Boston.