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Zhang, J.; Chen, W.; Zhong, J.; Tan, X.; Li, Y. (2006)
Publisher: Springer
Languages: English
Types: Article
Subjects: TK, QA75

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_ARTIFICIALINTELLIGENCE
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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