Ripple-Spreading Network Model Optimization by Genetic Algorithm

Table 1

Comparative results: to generate small-world network topology.

	NDT	Setup of model parameters	APL	ADEE	CC	ASSO
WS1	Circle dist.	𝑃RRLE = 0	12.8788	94.2	0.5000	0.0000
WS2	Circle dist.	𝑃RRLE = 0.08	4.4299	305.7	0.3117	0.0082
WS3	Circle dist.	𝑃RRLE = 1	3.5061	1325.0	0.0465	0.1795
WS4	Grid dist.	𝑃RRLE = 0	6.6667	222.2	0.0000	0. 4010
WS5	Grid dist.	𝑃RRLE = 0.08	4.2663	365.9	0.2399	0.0636
WS6	Grid dist.	𝑃RRLE = 1	3.5262	1024.0	0.0549	0.2630
WS7	Random	𝑃RRLE = 0	6.2159	239.9	0.3487	0.0557
WS8	Random	𝑃RRLE = 0.08	4.3079	362.3	0.2468	0.1269
WS9	Random	𝑃RRLE = 1	3.5132	1079.9	0.0758	0.2356
RSNM1	Circle dist.	𝛽𝑅 = 5.1, 𝛽𝐿 = 3.1, 𝛼 = 440.5, 𝑁SGR = 10, and 𝐸SGR(𝑖)∈[2489.6, 3507.7]	12.8788	94.2	0.5000	0.0000
RSNM2	Circle dist.	𝛽𝑅(𝑖)∈[0.6, 1.2], 𝛽𝐿(𝑖)∈[0.1, 0.8], 𝛼(𝑖)∈[482.4, 541.7], 𝑁SGR = 10, and 𝐸SGR(𝑖)∈[313.5, 650.0]	8.1432	151.9	0.4836	0.0598
RSBM3	Grid dist.	𝛽𝑅 = 4.5, 𝛽𝐿 = 4.1, 𝛼 = 1413.7, 𝑁SGR = 10, and 𝐸SGR(𝑖)∈[3927.3, 17181.8]	6.6667	222.2	0.0000	0.4010
RSNM4	Grid dist.	𝛽𝑅(𝑖)∈[4.5, 6.2], 𝛽𝐿(𝑖)∈[2.3, 7.5], 𝛼(𝑖)∈[1507.3, 1619.1], 𝑁SGR=10, and 𝐸SGR(𝑖)∈[3901.2, 17215.5]	4.9061	288.2	0.3396	0.0962
RSBM5	Random	𝛽𝑅 = 4.5, 𝛽𝐿 = 4.5, 𝛼 = 1319.5, 𝑁SGR = 10, and 𝐸SGR(𝑖)∈[3915.3, 17198.1]	5.4315	240.2	0.4348	0.1031
RSNM6	Random	𝛽𝑅(𝑖)∈[4.5, 6.1], 𝛽𝐿(𝑖)∈[1.9, 7.0], 𝛼(𝑖)∈[1853.8, 1527.2], 𝑁SGR = 10, and 𝐸SGR(𝑖)∈[3897.9, 17301.2]	4.0364	314.2	0.5542	0.1741