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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)

Abstract

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.

Keywords

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.

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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

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