A Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

Table 4

The best and mean values achieved via SaCDEhaS and four improvement DE algorithms within 1.5×10⁵ FEs.

Test Fun.		SaCDEhaS	SAMODE	DE-RHC	Adap.DE	ED-DE-MA
T01	Best	0.000000𝐸+00^∗	0.000000𝐸+00^∗	5.02𝐸−20^∗	0.000000𝐸+00^∗	0.0000𝐸+00^∗
	Mean	9.445299𝐸+00	1.212025𝐸+00	8.91𝐸+00	3.852582𝐸+00	0.0000𝐸+00
T02	Best	−2.842253𝐸+01^∗	−2.842253𝐸+01^∗	−2.84𝐸+01^∗	−2.842253𝐸+01^∗	−2.8423𝐸+01^∗
	Mean	−2.487860𝐸+01	−2.706978𝐸+01	−2.64𝐸+01	−2.678273𝐸+01	−2.6225𝐸+01
T03	Best	1.151489𝐸−05^∗	1.151489𝐸−05^∗	1.15𝐸−05^∗	1.151489𝐸−05^∗	1.1515𝐸−05^∗
	Mean	1.151489𝐸−05	1.151489𝐸−05	1.15𝐸−05	1.151489𝐸−05	1.1515𝐸−05
T04	Best	1.377076𝐸+01^∗	1.377076𝐸+01^∗	2.00𝐸+01	1.432911𝐸+01	1.3771𝐸+01^∗
	Mean	1.497052𝐸+01	1.394069𝐸+01	2.10𝐸+01	1.831217𝐸+01	1.3774𝐸+01
T05	Best	−3.596196𝐸+01	−3.684393𝐸+01	−3.69𝐸+01	−3.684537𝐸+01^∗	−3.6895𝐸+01
	Mean	−2.885426𝐸+01	−3.359474𝐸+01	−3.58𝐸+01	−3.381816𝐸+01	−3.3702𝐸+01
T06	Best	−2.680279𝐸+01	−2.916612𝐸+01^∗	−2.92𝐸+01^∗	−2.916612𝐸+01^∗	−2.9166𝐸+01^∗
	Mean	−2.030931𝐸+01	−2.763470𝐸+01	−2.91𝐸+01	−2.583055𝐸+01	−2.6809𝐸+01
T07	Best	5.000000𝐸−01^∗	5.000000𝐸−01^∗	9.51𝐸−01	5.000000𝐸−01^∗	5.1940𝐸−01
	Mean	7.904189𝐸−01	8.166238𝐸−01	1.15𝐸+01	5.000000𝐸−01	1.1875𝐸+00
T10	Best	−2.183457𝐸+01	−2.182166𝐸+01	−2.05𝐸+01	−2.180845𝐸+01	−2.1832𝐸+01
	Mean	−2.136730𝐸+01	−2.165891𝐸+01	−1.83𝐸+01	−2.095834𝐸+01	−2.1421𝐸+01
T12	Best	6.710520𝐸+00^∗	6.943215𝐸+00	1.58𝐸+01	1.239837𝐸+01	9.9697𝐸+00
	Mean	1.381452𝐸+01	1.106747𝐸+01	1.90𝐸+01	2.119566𝐸+01	1.4436𝐸+01
T13	Best	8.662559𝐸+00	8.610634𝐸+00	1.42𝐸+01	8.621241𝐸+00	1.4034𝐸+01
	Mean	1.854369𝐸+01	1.099524𝐸+01	2.00𝐸+01	1.253713𝐸+00	1.6241𝐸+01

∗Marks that the current best value is obtained.