@inproceedings{f0d176744c9543048fd8d39a4c2e904d,
title = "Continuous function optimization using hybrid ant colony approach with orthogonal design scheme",
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",
author = "Jun Zhang and Chen, {Wei Neng} and Zhong, {Jing Hui} and Xuan Tan and Yun Li",
year = "2006",
month = jan,
day = "1",
language = "English",
isbn = "3540473319",
volume = "4247 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "126--133",
booktitle = "Simulated Evolution and Learning - 6th International Conference, SEAL 2006, Proceedings",
note = "6th International Conference Simulated Evolution and Learning, SEAL 2006 ; Conference date: 15-10-2006 Through 18-10-2006",
}