Research Article
A Bio-Inspired Method for the Constrained Shortest Path Problem
Algorithm 1
A bioinspired method for the constrained shortest path problem.
| // is the distance matrix, represents the edge length from node to node . | | // is the cost matrix, represents the edge cost from node to node . | | // is the source node, is the sink node | | // : represents the increasing times for the segment with flux continuing to grow in the network (growing segment). | | // : represents the whose value is beyond the threshold (potential segment). | | // is the constraint, is the threshold, is the divisor of punishment | | | | | | while do | | // The pressure at the ending node | | Calculate the pressure of every node using (11) | | | | // Using (6) | | if then | | ; //Using (12) | | ; | | else | | ; | | end if | | if then | | | | else | | | | end if | | if then | | | | else | | | | end if | | if judgePath() //To judge wether there is a path, the result is boolean then | | = findPath() //Find the path and return. The nodes (important segment) making up the important path are saved. | | if calculateCost( //To judge wether the cost of the path is beyond the constraint then | | for to (length(paths) − 1) do | | | | | | | | | | end for | | else | | | | end if | | end if | | end while |
|
