Continuous Function Optimization Using Hybrid Ant Colony Approach with Orthogonal Design Scheme

  • Jun Zhang
  • Wei-neng Chen
  • Jing-hui Zhong
  • Xuan Tan
  • Yun Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is dispersed into a number of random and movable nodes; b) the carriers of pheromone of ACO are shifted to the nodes; c) solution path can be obtained by choosing one appropriate node from each IVS by ant; d) with the ODS, the best solved path is further improved. The proposed algorithm has been successfully applied to 10 benchmark test functions. The performance and a comparison with CACO and FEP have been studied.


Search Range Solution Path Unimodal Function Multimodal Function Pheromone Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. on systems man, and cybernetics - part B: cybernetics 26, 29–41 (1996)CrossRefGoogle Scholar
  2. 2.
    Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to TSP. IEEE Trans. Evol. Comput. 1, 53–66 (1997)CrossRefGoogle Scholar
  3. 3.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(2), 137–172 (1999)CrossRefGoogle Scholar
  4. 4.
    Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 4, 321–332 (2002)CrossRefGoogle Scholar
  5. 5.
    Sim, K.M., Sun, W.H.: Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Trans. on systems man, and cybernetics - part A: system and humans 33, 560–572 (2003)CrossRefGoogle Scholar
  6. 6.
    Bilchev, G., Parmee, I.C.: The ant colony metaphor for searching continuous design spaces. In: AISB Workshop on evolutionary computation (1995)Google Scholar
  7. 7.
    Wodrich, M., Bilchev, G.: Corporative distributed search: the ants’ way. Control Cybernetics 26, 413 (1997)zbMATHMathSciNetGoogle Scholar
  8. 8.
    Mathur, M., Karale, S.B., Priye, S., Jayaraman, V.K., Kulkarni, B.D.: Ant colony approach to continuous function optimization. Ind. Eng. Chem. Res., 3814–3822 (2000)Google Scholar
  9. 9.
    Monmarché, N., Venturini, G., Slimane, M.: On how Pachycondyla apicalis ants suggest a new search algorithm. Future Generation Computer Systems 16, 937–946 (2000)CrossRefGoogle Scholar
  10. 10.
    Dréo, J., Siarry, P.: Continues interacting ant colony algorithm based on dense heterarchy. Future Generation Computer Systems 20(5), 841–856 (2004)CrossRefGoogle Scholar
  11. 11.
    Fang, K.T., Wang, Y.: Number-Theoretic Methods in Statistics. Chapman & Hall, New York (1994)zbMATHGoogle Scholar
  12. 12.
    Hedayat, A.S., Solane, N.J.A., Stufken, J.: Orthogonal Arrays: Theory and Applications. Springer, New York (1999)zbMATHGoogle Scholar
  13. 13.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 8, 456–470 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Zhang
    • 1
  • Wei-neng Chen
    • 1
  • Jing-hui Zhong
    • 1
  • Xuan Tan
    • 1
  • Yun Li
    • 2
  1. 1.Department of Computer ScienceSun Yat-sen UniversityP.R. China
  2. 2.Department of Electronics and Electrical EngineeringUniversity of GlasgowUK

Personalised recommendations