Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train

Table 2

Comparison of the results between ACS and ACSd.

Instances	Algorithm	Opt.	Max				Min	Err. (%)	S.t.
eil51	ACS	426	455.152	453.881	449.160	446.438	443.176	4.0	3.25
	ACSd		429.484	429.032	428.982	428.982	428.872	0.7	0.15
KroA100	ACS	21282	22884.7	22689.2	22479.2	22374.4	22300.1	4.8	151.12
	ACSd		21628	21587.8	21307.4	21285.4	21285.4	0.0	122.59
eil101	ACS	629	704.712	697.434	690.385	677.283	671.198	6.7	8.74
	ACSd		665.174	655.826	650.095	644.975	642.866	2.2	5.33
d198	ACS	15780	17099.30	17069.70	16818.85	16537.02	16456.50	4.3	194.23
	ACSd		16163.30	16071.74	16009.60	15963.46	15933.20	1.0	51.28
KroA200	ACS	29368	32305.10	32186.30	31802.30	31286.43	31209.70	6.3	329.95
	ACSd		30098.60	29922.71	29667.55	29518.40	29469.00	0.3	160.66
d493	ACS	35002	40688.80	40403.00	40117.25	39860.34	39687.10	13.4	251.03
	ACSd		39895.80	39654.84	39286.60	38747.83	38395.60	9.7	378.12