Tuning Genetic Algorithm Parameters to Improve Convergence Time

Table 4

Results from model parameter identification using different kinds of SGA.

	SGA-SCM		SGA-CMS		SGA-MCS		SGA-SMC
	𝐽	𝑡[𝑠]	𝐽	𝑡[𝑠]	𝐽	𝑡[𝑠]	𝐽	𝑡[𝑠]
GGAP
0.5	0.0223	43.8120	0.0222	38.1710	0.0221	37.2810	0.0221	44.5160
0.67	0.0221	52.7970	0.0225	37.1870	0.0223	39.7810	0.0221	55.0460
0.8	0.0221	67.9220	0.0222	50.2970	0.0224	53.4530	0.0221	67.6410
0.9	0.0222	70.6250	0.0224	52.3440	0.0230	61.4370	0.0222	70.9690
XOVR
0.65	0.0222	65.5930	0.0223	45.9680	0.0221	50.0930	0.0222	69.6560
0.75	0.0223	60.7650	0.0223	44.8590	0.0223	47.7350	0.0221	65.9530
0.85	0.0221	65.6250	0.0224	45.9220	0.0227	41.5310	0.0221	61.6100
0.95	0.0221	65.6090	0.0224	45.9220	0.0225	50.5000	0.0222	63.1720
MUTR
0.02	0.0222	56.2190	0.0228	46.5150	0.0222	48.6570	0.0222	55.1090
0.04	0.0221	62.0000	0.0221	44.3120	0.0223	50.1250	0.0221	56.1880
0.06	0.0223	70.9530	0.0222	42.7970	0.0221	45.5470	0.0222	65.3280
0.08	0.0222	81.0470	0.0224	49.0000	0.0222	43.8430	0.0221	76.2030
0.1	0.0221	82.5150	0.0221	42.5160	0.0226	40.9370	0.0222	83.0310