Continuous function optimization using hybrid ant colony approach with orthogonal design scheme

Authors:
Jun Zhang;Wei-neng Chen;Jing-hui Zhong;Xuan Tan;Yun Li
Affiliations:
Department of Computer Science, Sun Yat-sen University, P.R. China;Department of Computer Science, Sun Yat-sen University, P.R. China;Department of Computer Science, Sun Yat-sen University, P.R. China;Department of Computer Science, Sun Yat-sen University, P.R. China;Department of Electronics and Electrical Engineering, University of Glasgow, UK
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

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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.