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

Jun Zhang, Wei Neng Chen, Jing Hui Zhong, Xuan Tan, Yun Li

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

12 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationSimulated Evolution and Learning - 6th International Conference, SEAL 2006, Proceedings
PublisherSpringer-Verlag
Pages126-133
Number of pages8
Volume4247 LNCS
ISBN (Print)3540473319, 9783540473312
Publication statusPublished - 1 Jan 2006
Event6th International Conference Simulated Evolution and Learning, SEAL 2006 - Hefei, China
Duration: 15 Oct 2006 → 18 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4247 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference Simulated Evolution and Learning, SEAL 2006
CountryChina
CityHefei
Period15/10/06 → 18/10/06

Keywords

  • search range
  • solution path
  • unimodal function
  • multimodal function
  • pheromone information

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    Zhang, J., Chen, W. N., Zhong, J. H., Tan, X., & Li, Y. (2006). Continuous function optimization using hybrid ant colony approach with orthogonal design scheme. In Simulated Evolution and Learning - 6th International Conference, SEAL 2006, Proceedings (Vol. 4247 LNCS, pp. 126-133). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4247 LNCS). Springer-Verlag.