Improved ant colony optimization algorithm based on RNA computing
- Authors: Zhang L.1, Xiao C.2, Fei T.1
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Affiliations:
- School of Information Engineering
- School of Economics
- Issue: Vol 51, No 5 (2017)
- Pages: 366-375
- Section: Article
- URL: https://journal-vniispk.ru/0146-4116/article/view/174950
- DOI: https://doi.org/10.3103/S0146411617050108
- ID: 174950
Cite item
Abstract
RNA computing is a new intelligent optimization algorithm, which combines computer science and molecular biology. Aiming at the weakness of slow convergence rate and poor global search ability in the basic ant colony optimization algorithm due to the unreasonable selection of parameters, this paper utilizes the combination of RNA computing and basic ant colony optimization algorithm to overcome the defects. An improved ant colony optimization algorithm based on RNA computing is proposed. In the iterative process of ant colony optimization algorithm, transformation operation, recombination operation and permutation operation in RNA computing are introduced to optimize the initial parameters including importance factor of pheromone trail α, importance factor of heuristic function β and pheromone evaporation rate ρ to improve the convergence efficiency and global search ability. The performance of the algorithm is evaluated on five instances of the library of traveling salesman problems (TSPLIB) and six typical test functions. The experimental results demonstrate that the proposed RNA-ant colony optimization algorithm is superior than basic ant colony optimization algorithm in optimization ability, reliability, convergence efficiency, stability and robustness.
About the authors
Liyi Zhang
School of Information Engineering
Email: fei_8825@163.com
China, Tianjin, 300134
Chao Xiao
School of Economics
Email: fei_8825@163.com
China, Tianjin, 300134
Teng Fei
School of Information Engineering
Author for correspondence.
Email: fei_8825@163.com
China, Tianjin, 300134
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