• DocumentCode
    1844809
  • Title

    Hybrid Algorithm Combining Ant Colony Algorithm with Genetic Algorithm for Continuous Domain

  • Author

    Liu, Bo ; Meng, Peisheng

  • Author_Institution
    Dept. of Mech., Huazhong Univ. of Sci. & Technol., Wuhan
  • fYear
    2008
  • fDate
    18-21 Nov. 2008
  • Firstpage
    1819
  • Lastpage
    1824
  • Abstract
    Ant colony algorithm is a kind of new heuristic biological modeling method which has the ability of parallel processing and global searching. By use of the properties of ant colony algorithm and genetic algorithm, the hybrid algorithm which adopts genetic algorithm to distribute the original pheromone is proposed to solve the continuous optimization problem. Several solutions are obtained using the ant colony algorithm through pheromone accumulation and renewal. Finally, by using crossover and mutation operation of genetic algorithm, some effective solutions are obtained. The results of experiments show better performances of the new algorithm based on six continuous test functions compared with the methods available in literature.
  • Keywords
    genetic algorithms; parallel algorithms; search problems; ant colony algorithm; continuous domain; continuous optimization problem; crossover operation; genetic algorithm; heuristic biological modeling method; mutation operation; parallel processing; Ant colony optimization; Biological system modeling; Biology computing; Feedback; Genetic algorithms; Genetic mutations; Parallel processing; Particle swarm optimization; Performance evaluation; Testing; Ant Colony Algorithm (ACA); Genetic Algorithm (GA); Hybrid algorithm; continuous optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference for
  • Conference_Location
    Hunan
  • Print_ISBN
    978-0-7695-3398-8
  • Electronic_ISBN
    978-0-7695-3398-8
  • Type

    conf

  • DOI
    10.1109/ICYCS.2008.12
  • Filename
    4709250