Reference: Hu X B, Wang M, Leeson M S, et al. Deterministic agent-based path optimization by mimicking the spreading of ripples[J]. Evolutionary Computation, 2016, 24(2): 319-346.
Variables | Meaning |
---|---|
network | Dictionary, {node1: {node2: length, node3: length, ...}, ...} |
source | The source node |
destination | The destination node |
nn | The number of nodes |
neighbor | Dictionary, {node1: [the neighbor nodes of node1], ...} |
v | The ripple-spreading speed (i.e., the minimum length of arcs) |
t | The simulated time index |
nr | The number of ripples - 1 |
epicenter_set | List, the epicenter node of the ith ripple is epicenter_set[i] |
path_set | List, the path of the ith ripple from the source node to node i is path_set[i] |
radius_set | List, the radius of the ith ripple is radius_set[i] |
active_set | List, active_set contains all active ripples |
Omega | Dictionary, Omega[n] = i denotes that ripple i is generated at node n |
if __name__ == '__main__':
test_network = {
0: {1: 62, 2: 44, 3: 67},
1: {0: 62, 2: 32, 4: 52},
2: {0: 44, 1: 33, 3: 32, 4: 52},
3: {0: 67, 2: 32, 4: 54},
4: {1: 52, 2: 52, 3: 54}
}
source = 0
destination = 4
print(main(test_network, source, destination))