An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization

Table 2

The experiment result of comparison of adaptive Cauchy DE with other DE algorithms.

	GEN	Adaptive Cauchy DE		DE/rand/1/bin		jDE		SaDE		MDE
		Mean	Std	Mean	Std	Mean	Std	Mean	Std	Mean	Std
𝐹1	1500	𝟓.𝟎𝐄−𝟑𝟔	𝟗.𝟒𝐄−𝟑𝟔	7.9𝐸−14	6.8𝐸−14	2.6𝐸−28	4.0𝐸−28	1.8𝐸−20	2.3𝐸−20	7.0𝐸−17	2.8𝐸−17
𝐹2	2000	𝟐.𝟒𝐄−𝟑𝟎	𝟏.𝟔𝐄−𝟑𝟎	1.2𝐸−09	6.7𝐸−10	1.8𝐸−23	1.8𝐸−23	6.2𝐸−15	3.2𝐸−15	4.8𝐸−13	1.3𝐸−13
𝐹3	5000	2.9𝐸−12	1.1𝐸−12	3.3𝐸−02	7.2𝐸−02	𝟐.𝟎𝐄−𝟏𝟓	𝟑.𝟑𝐄−𝟏𝟓	7.8𝐸−10	2.0𝐸−10	2.0𝐸−08	8.5𝐸−09
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	4.6𝐸−03	1.0𝐸−03	3.1𝐸−03	8.5𝐸−04	4.5𝐸−03	1.2𝐸−03	8.8𝐸−03	1.8𝐸−03
𝐹6	9000	−𝟏𝟐𝟓𝟔𝟗.𝟓	𝟕.𝟑𝐄−𝟏𝟐	−11095.3	5.2𝐸+02	−𝟏𝟐𝟓𝟔𝟗.𝟓	𝟕.𝟑𝐄−𝟏𝟐	−𝟏𝟐𝟓𝟔𝟗.𝟓	𝟕.𝟑𝐄−𝟏𝟐	−11482.1	3.0𝐸+02
𝐹7	5000	𝟎.𝟎𝐄+𝟎𝟎	𝟎.𝟎𝐄+𝟎𝟎	7.1𝐸+01	2.9𝐸+01	𝟎.𝟎𝐄+𝟎𝟎	𝟎.𝟎𝐄+𝟎𝟎	𝟎.𝟎𝐄+𝟎𝟎	𝟎.𝟎𝐄+𝟎𝟎	4.0𝐸+01	5.6𝐸+00
𝐹8	1500	𝟑.𝟑𝐄−𝟏𝟓	𝟕.𝟎𝐄−𝟏𝟔	9.2𝐸−08	4.0𝐸−08	8.2𝐸−15	2.3𝐸−15	5.3𝐸−11	3.7𝐸−11	4.0𝐸−09	9.2𝐸−10
𝐹9	2000	𝟎.𝟎𝐄+𝟎𝟎	𝟎.𝟎𝐄+𝟎𝟎	3.9𝐸−04	2.0𝐸−03	𝟎.𝟎𝐄+𝟎𝟎	𝟎.𝟎𝐄+𝟎𝟎	𝟎.𝟎𝐄+𝟎𝟎	𝟎.𝟎𝐄+𝟎𝟎	7.4𝐸−03	1.1𝐸−02
𝐹10	1500	𝟏.𝟔𝐄−𝟑𝟐	𝟓.𝟓𝐄−𝟒𝟖	6.5𝐸−15	5.6𝐸−15	7.0𝐸−30	7.2𝐸−30	3.3𝐸−20	4.2𝐸−20	9.9𝐸−18	1.5𝐸−17
𝐹11	1500	𝟏.𝟑𝐄−𝟑𝟐	𝟏.𝟏𝐄−𝟒𝟕	5.9𝐸−14	4.9𝐸−14	1.2𝐸−28	1.4𝐸−28	8.5𝐸−20	1.6𝐸−19	2.6𝐸−17	1.6𝐸−17
𝐹12	3000	𝟔.𝟏𝐄−𝟐𝟐	𝟓.𝟎𝐄−𝟐𝟐	2.1𝐸−07	1.3𝐸−07	6.3𝐸−17	7.0𝐸−17	3.5𝐸−12	2.9𝐸−12	7.4𝐸−11	3.9𝐸−11
𝐹13	1000	𝟓.𝟎𝐄−𝟐𝟎	𝟑.𝟒𝐄−𝟐𝟎	7.3𝐸−04	6.6𝐸−04	1.4𝐸−15	6.9𝐸−16	1.5𝐸−07	3.4𝐸−08	3.4𝐸−05	1.3𝐸−05
𝐹14	3000	𝟑.𝟓𝐄−𝟐𝟐	𝟐.𝟓𝐄−𝟐𝟐	2.3𝐸−05	1.7𝐸−05	7.5𝐸−17	1.4𝐸−16	1.8𝐸−10	2.2𝐸−10	1.2𝐸−08	3.7𝐸−09