TY - GEN
T1 - A novel pheromone initialization strategy of ACO algorithms for solving TSP
AU - Gao, Shupeng
AU - Zhong, Jiaqi
AU - Cui, Yali
AU - Gao, Chao
AU - Li, Xianghua
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/21
Y1 - 2018/6/21
N2 - Travelling salesman problem (TSP), as a famous combinational optimization problem, has promoted the generation of a large number of algorithms. However, the existing algorithms, such as ant colony optimization (ACO) algorithms, still need to be enhanced further in terms of their robustness and the quality of the solution. In this paper, a novel pheromone initialization (NPI) strategy of ACO algorithms has been proposed for solving TSP, which shows a better efficiency in both robustness and the quality of the solution. Combining NPI strategy with a typical ACO algorithm like ant colony system (ACS) algorithm, a novel algorithm, called NPI-ACS algorithm, is put forward to strengthen the efficiency of ACS. Meanwhile, seven different scale datasets related to TSP are used to estimate the performance of NPI strategy. Afterwards, the experimental results show that there is a remarkable improvement in terms of robustness and the quality of the solution. Moreover, the proposed NPI strategy is flexible enough to be combined with multifarious ACO algorithms for solving TSP because of its independence in operation details.
AB - Travelling salesman problem (TSP), as a famous combinational optimization problem, has promoted the generation of a large number of algorithms. However, the existing algorithms, such as ant colony optimization (ACO) algorithms, still need to be enhanced further in terms of their robustness and the quality of the solution. In this paper, a novel pheromone initialization (NPI) strategy of ACO algorithms has been proposed for solving TSP, which shows a better efficiency in both robustness and the quality of the solution. Combining NPI strategy with a typical ACO algorithm like ant colony system (ACS) algorithm, a novel algorithm, called NPI-ACS algorithm, is put forward to strengthen the efficiency of ACS. Meanwhile, seven different scale datasets related to TSP are used to estimate the performance of NPI strategy. Afterwards, the experimental results show that there is a remarkable improvement in terms of robustness and the quality of the solution. Moreover, the proposed NPI strategy is flexible enough to be combined with multifarious ACO algorithms for solving TSP because of its independence in operation details.
KW - Ant Colony Optimization
KW - Pheromone Initialization Strategy
KW - TSP
UR - https://www.scopus.com/pages/publications/85050201232
U2 - 10.1109/FSKD.2017.8393155
DO - 10.1109/FSKD.2017.8393155
M3 - 会议稿件
AN - SCOPUS:85050201232
T3 - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
SP - 243
EP - 248
BT - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
A2 - Zhao, Liang
A2 - Wang, Lipo
A2 - Cai, Guoyong
A2 - Li, Kenli
A2 - Liu, Yong
A2 - Xiao, Guoqing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
Y2 - 29 July 2017 through 31 July 2017
ER -