An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization

Table 6

The experiment result of comparison of various failure counters.

	GEN	FC𝐹=0,FCCR=0		FC𝐹=0,FCCR=1		FC𝐹=1,FCCR=0		FC𝐹=1,FCCR=1		FC𝐹=2,FCCR=2
		Mean	Std	Mean	Std	Mean	Std	Mean	Std	Mean	Std
𝐹1	1500	5.0𝐸−36	9.4𝐸−36	4.0𝐸−31	5.2𝐸−31	𝟔.𝟗𝐄−𝟒𝟎	𝟔.𝟖𝐄−𝟒𝟎	2.1𝐸−35	4.1𝐸−35	1.4𝐸−30	2.0𝐸−30
𝐹2	2000	2.4𝐸−30	1.6𝐸−30	1.7𝐸−26	1.5𝐸−26	𝟖.𝟕𝐄−𝟑𝟒	𝟓.𝟐𝐄−𝟑𝟒	3.4𝐸−30	3.1𝐸−30	2.0𝐸−26	1.4𝐸−26
𝐹3	5000	𝟐.𝟗𝐄−𝟏𝟐	𝟏.𝟏𝐄−𝟏𝟐	1.9𝐸−04	3.0𝐸−05	2.5𝐸+00	2.9𝐸+00	4.7𝐸−01	9.7𝐸−01	1.1𝐸+00	1.5𝐸+00
𝐹4	1500	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00
𝐹5	3000	𝟑.𝟎𝐄−𝟎𝟑	𝟔.𝟗𝐄−𝟎𝟒	3.1𝐸−03	8.4𝐸−04	3.1𝐸−03	8.9𝐸−04	3.0𝐸−03	8.0𝐸−04	3.1𝐸−03	8.3𝐸−04
𝐹6	9000	−𝟏𝟐𝟓𝟔𝟗.𝟓	𝟕.𝟑𝐄−𝟏𝟐	−𝟏𝟐𝟓𝟔𝟗.𝟓	𝟕.𝟑𝐄−𝟏𝟐	−𝟏𝟐𝟓𝟔𝟗.𝟓	𝟕.𝟑𝐄−𝟏𝟐	−𝟏𝟐𝟓𝟔𝟗.𝟓	𝟕.𝟑𝐄−𝟏𝟐	−12567.1	1.7𝐸+01
𝐹7	5000	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00
𝐹8	1500	𝟑.𝟑𝐄−𝟏𝟓	𝟕.𝟎𝐄−𝟏𝟔	1.4𝐸−14	4.5𝐸−15	3.1𝐸−15	0.0𝐸+00	3.0𝐸−07	2.1𝐸−06	1.5𝐸−14	2.6𝐸−15
𝐹9	2000	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00	0.0𝐸+00
𝐹10	1500	𝟏.𝟔𝐄−𝟑𝟐	𝟓.𝟓𝐄−𝟒𝟖	9.6𝐸−32	6.6𝐸−32	4.8𝐸−07	3.4𝐸−06	1.6𝐸−32	3.6𝐸−34	1.7𝐸−31	1.6𝐸−31
𝐹11	1500	𝟏.𝟑𝐄−𝟑𝟐	𝟏.𝟏𝐄−𝟒𝟕	7.3𝐸−31	1.1𝐸−30	1.4𝐸−32	6.9𝐸−34	1.3𝐸−32	1.1𝐸−47	8.5𝐸−31	7.8𝐸−31
𝐹12	3000	6.1𝐸−22	5.0𝐸−22	1.6𝐸−18	1.8𝐸−18	𝟏.𝟒𝐄−𝟐𝟑	𝟔.𝟗𝐄−𝟐𝟑	2.0𝐸−21	2.1𝐸−21	1.4𝐸−18	1.4𝐸−18
𝐹13	1000	5.0𝐸−20	3.4𝐸−20	1.7𝐸−16	1.4𝐸−16	𝟓.𝟖𝐄−𝟐𝟑	𝟑.𝟑𝐄−𝟐𝟑	8.6𝐸−20	5.9𝐸−20	4.7𝐸−17	4.1𝐸−17
𝐹14	3000	3.5𝐸−22	2.5𝐸−22	7.2𝐸−19	7.1𝐸−19	𝟗.𝟓𝐄−𝟐𝟓	𝟏.𝟏𝐄−𝟐𝟒	8.0𝐸−22	8.9𝐸−22	8.3𝐸−19	6.6𝐸−19