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Volume 23 Issue 1
January  2008
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Xiao-Min Hu, Jun Zhang, Yun Li. Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems[J]. Journal of Computer Science and Technology, 2008, 23(1): 2-0.
Citation: Xiao-Min Hu, Jun Zhang, Yun Li. Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems[J]. Journal of Computer Science and Technology, 2008, 23(1): 2-0.
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Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems

  • 1Department of Computer Science, Sun Yat-Sen University, Guangzhou 510275, China2Key Laboratory of Digital Life (Sun Yat-Sen University), Ministry of Education, Guangzhou 510275, China3Department of Electronics and Electrical Engineering, University of Glasgow, Glasgow G12 8LT, Scotland, U.K.

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  • Revised Date: November 05, 2007
  • Published Date: January 14, 2008
    Abstract
  • Research into ant colony algorithms for solvingcontinuous optimization problems forms one of the most significant andpromising areas in swarm computation. Although traditional antalgorithms are designed for combinatorial optimization, they have showngreat potential in solving a wide range of optimization problems,including continuous optimization. Aimed at solving continuous problemseffectively, this paper develops a novel ant algorithm termed``continuous orthogonal ant colony'' (COAC), whose pheromone depositmechanisms would enable ants to search for solutions collaborativelyand effectively. By using the orthogonal design method, ants in thefeasible domain can explore their chosen regions rapidly andefficiently. By implementing an ``adaptive regional radius'' method, theproposed algorithm can reduce the probability of being trapped in localoptima and therefore enhance the global search capability and accuracy.An elitist strategy is also employed to reserve the most valuablepoints. The performance of the COAC is compared with two other antalgorithms for continuous optimization --- API and CACO by testingseventeen functions in the continuous domain. The results demonstratethat the proposed COAC algorithm outperforms the others.
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