A Novel Approach to Improve the Performance of Evolutionary Methods for Nonlinear Constrained Optimization

Table 2

Summary of eleven test problems [10].

	Type of problem	n	Type of f	Linear inequality	Nonlinear equality	Nonlinear inequality	|𝐹|/|𝑆|	Optimum value
G1	Minimum	13	Quadratic	9	0	0	0.0111%	−15.0
G2	Maximum	20	Nonlinear	0	0	2	99.8474%	0.803553
G3	Maximum	10	Polynomial	0	1	0	0.0000%	1.0
G4	Minimum	5	Quadratic	0	0	6	52.1230%	−30655.5
G5	Minimum	4	Cubic	2	3	0	0.0000%	5126.4981
G6	Minimum	2	Cubic	0	0	2	0.0066%	−6961.8
G7	Minimum	10	Quadratic	3	0	5	0.0003%	24.306
G8	Maximum	2	Nonlinear	0	0	2	0.8560%	0.0958250
G9	Minimum	7	Polynomial	0	0	4	0.5121%	680.63
G10	Minimum	8	Linear	3	0	3	0.0010%	7049.33
G11	Minimum	2	Quadratic	0	1	0	0.0000%	0.75