Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches

Table 11

Comparison of the numerical results of the proposed RGA-PSO and AIA-PSO algorithms and those of the published hybrid algorithms for TPs 12-13.

TP number	Methods	Best	Mean	Median	Worst	S.D.
12	CDE [23]	0.0126702	0.012703	—	0.012790	2.7𝐸−05
	NM-PSO [24]	0.0126302	0.0126314	—	0.012633	8.73𝐸−07
	The proposed RGA-PSO	0.012692	0.012724	0.012721	0.012784	1.46𝐸−05
	The proposed AIA-PSO	0.012667	0.012715	0.012719	0.012778	2.00𝐸−05
13	CDE [23]	6059.7340	6085.2303	—	6371.0455	43.01
	NM-PSO [24]	5930.3137	5946.7901	—	5960.0557	9.16
	The proposed RGA-PSO	5885.3018	5895.0381	5885.3326	6005.4351	24.33
	The proposed AIA-PSO	5885.3310	5886.5426	5885.3323	5906.7404	4.54