TY - JOUR
T1 - Rapid Physarum Algorithm for shortest path problem
AU - Zhang, Xiaoge
AU - Zhang, Yajuan
AU - Zhang, Zili
AU - Mahadevan, Sankaran
AU - Adamatzky, Andrew
AU - Deng, Yong
N1 - Funding Information:
The authors greatly appreciate the reviews’ suggestions. The work is partially supported by National Natural Science Foundation of China (Grant No. 61174022 ), Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20131102130002 ), R&D Program of China ( 2012BAH07B01 ), National High Technology Research and Development Program of China (863 Program) (Grant No. 2013AA013801 ), the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. BUAA-VR-14KF-02 ).
PY - 2014/10
Y1 - 2014/10
N2 - As shortest path (SP) problem has been one of the most fundamental network optimization problems for a long time, technologies for this problem are still being studied. In this paper, a new method by integrating a path finding mathematical model, inspired by Physarum polycephalum, with extracted one heuristic rule to solve SP problem has been proposed, which is called Rapid Physarum Algorithm (RPA). Simulation experiments have been carried out on three different network topologies with varying number of nodes. It is noted that the proposed RPA can find the optimal path as the path finding model does for most networks. What is more, experimental results show that the performance of RPA surpasses the path finding model on both iterations and solution time.
AB - As shortest path (SP) problem has been one of the most fundamental network optimization problems for a long time, technologies for this problem are still being studied. In this paper, a new method by integrating a path finding mathematical model, inspired by Physarum polycephalum, with extracted one heuristic rule to solve SP problem has been proposed, which is called Rapid Physarum Algorithm (RPA). Simulation experiments have been carried out on three different network topologies with varying number of nodes. It is noted that the proposed RPA can find the optimal path as the path finding model does for most networks. What is more, experimental results show that the performance of RPA surpasses the path finding model on both iterations and solution time.
KW - Heuristic rule
KW - Physarum polycephalum
KW - Rapid Physarum Algorithm
KW - Shortest path problem
UR - http://www.scopus.com/inward/record.url?scp=84903626298&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2014.05.032
DO - 10.1016/j.asoc.2014.05.032
M3 - Journal article
AN - SCOPUS:84903626298
SN - 1568-4946
VL - 23
SP - 19
EP - 26
JO - Applied Soft Computing Journal
JF - Applied Soft Computing Journal
ER -