TY - GEN
T1 - A new physarum network based genetic algorithm for bandwidth-delay constrained least-cost multicast routing
AU - Liang, Mingxin
AU - Gao, Chao
AU - Liu, Yuxin
AU - Tao, Li
AU - Zhang, Zili
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Bandwidth-delay constrained least-cost multicast routing is a typical NP-complete problem. Although some swarm-based intelligent algorithms (e.g., genetic algorithm (GA)) are proposed to solve this problem, the shortcomings of local search affect the computational effectiveness. Taking the ability of building a robust network of Physarum network model (PN), a new hybrid algorithm, Physarum network-based genetic algorithm (named as PNGA), is proposed in this paper. In PNGA, an updating strategy based on PN is used for improving the crossover operator of traditional GA, in which the same parts of parent chromosomes are reserved and the new offspring by the Physarum network model is generated. In order to estimate the effectiveness of our proposed optimized strategy, some typical genetic algorithms and the proposed PNGA are compared for solving multicast routing. The experiments show that PNGA has more efficient than original GA. More importantly, the PNGA is more robustness that is very important for solving the multicast routing problem.
AB - Bandwidth-delay constrained least-cost multicast routing is a typical NP-complete problem. Although some swarm-based intelligent algorithms (e.g., genetic algorithm (GA)) are proposed to solve this problem, the shortcomings of local search affect the computational effectiveness. Taking the ability of building a robust network of Physarum network model (PN), a new hybrid algorithm, Physarum network-based genetic algorithm (named as PNGA), is proposed in this paper. In PNGA, an updating strategy based on PN is used for improving the crossover operator of traditional GA, in which the same parts of parent chromosomes are reserved and the new offspring by the Physarum network model is generated. In order to estimate the effectiveness of our proposed optimized strategy, some typical genetic algorithms and the proposed PNGA are compared for solving multicast routing. The experiments show that PNGA has more efficient than original GA. More importantly, the PNGA is more robustness that is very important for solving the multicast routing problem.
KW - Genetic algorithm
KW - Multicast routing
KW - Physarum network model
UR - https://www.scopus.com/pages/publications/84947746259
U2 - 10.1007/978-3-319-20472-7_29
DO - 10.1007/978-3-319-20472-7_29
M3 - 会议稿件
AN - SCOPUS:84947746259
SN - 9783319204710
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 273
EP - 280
BT - Advances in Swarm and Computational Intelligence - 6th International Conference, ICSI 2015 held in conjunction with the 2nd BRICS Congress, CCI 2015, Proceedings
A2 - Tan, Ying
A2 - Buarque, Fernando
A2 - Engelbrecht, Andries
A2 - Gelbukh, Alexander
A2 - Das, Swagatam
A2 - Shi, Yuhui
PB - Springer Verlag
T2 - 6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015
Y2 - 25 June 2015 through 28 June 2015
ER -