The results of all presented ΔQ methods (A, B, C and D) are compared to the results obtained using the EPANET2 hydraulic solver (R). The first tests of the ΔQ method implementation, including all variations (A, B, C and D) were run on the NYT network. These different approaches share the same modified FBN (Figure 5(b)). There are two loops in the network: the larger loop 1 contains a reservoir along with the nodes numbered from 2 to 15, while loop 2 has nodes 11, 9, 16 and 20. The location of the split is arbitrary and inconsequential. Loop 1 was split in the proximity of node 6 where a new node 6′ is introduced as a start node for the downstream pipe. Loop 2 was split close to node 20, with the new node 20′ being generated. Flow corrections for loops 1 and 2, ΔQ1 and ΔQ2, respectively, are introduced as demands in the nodes 6 and 20, and in the nodes 6′ and 20′ as negative demands or inflows. A comparison of obtained results, for 1,000 generations and population of 100, is given in Table 2.

Table 2

A comparison of the optimization algorithms performance indicators for NYT

GA optimization algorithmFitness function f [106 $]CPU time t [s]Speedup factor [-]
EPANET2 DLL (R) 38.6 390 
Upgraded ΔQ (A) 38.6 18.5 21.1 
Fixed ΔQ (B) 40.2 78.0 
Variable ΔQ (C) 39.8 5.5 70.9 
Fixed iteration ΔQ (D) 39.0 18.4 21.2 
GA optimization algorithmFitness function f [106 $]CPU time t [s]Speedup factor [-]
EPANET2 DLL (R) 38.6 390 
Upgraded ΔQ (A) 38.6 18.5 21.1 
Fixed ΔQ (B) 40.2 78.0 
Variable ΔQ (C) 39.8 5.5 70.9 
Fixed iteration ΔQ (D) 39.0 18.4 21.2 

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