Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems

Table 5

Optimization results for 5 schemes of the warehouse scheduling problem. Here denotes the mean value and corresponding standard deviation of the scheduling quality effect. The best results in each row are shown in boldface font.

Problem		S-SGA	M-SGA	S-PBIL	M-PBIL
Scheme 1	(20, 20)	2629.3 (75.4)	2317.7 (47.6)	2613.1 (55.9)	2304.1 (28.3)
Scheme 2	(20, 16)	1627.1 (66.2)	1423.7 (48.6)	1599.4 (57.3)	1398.4 (66.4)
Scheme 3	(20, 12)	914.5 (32.7)	852.3 (53.2)	973.4 (44.1)	886.0 (53.7)
Scheme 4	(16, 20)	1533.2 (82.1)	1295.4 (59.6)	1436.1 (72.4)	1266.7 (36.7)
Scheme 5	(12, 20)	884.4 (26.4)	712.6 (52.0)	804.3 (36.7)	699.4 (45.1)
Problem		S-SaDE	M-SaDE	S-PSO2011	M-PSO2011
Scheme 1	(20, 20)	2322.5 (79.3)	2003.7 (56.6)	2395.6 (47.5)	2113.8 (39.1)
Scheme 2	(20, 16)	1453.6 (46.2)	1205.4 (47.3)	1490.1 (29.3)	1347.6 (61.7)
Scheme 3	(20, 12)	812.6 (39.8)	705.3 (55.4)	884.2 (16.7)	783.3 (24.5)
Scheme 4	(16, 20)	1233.8 (38.0)	1100.9 (41.2)	1312.5 (53.4)	1194.2 (29.9)
Scheme 5	(12, 20)	711.5 (36.7)	621.8 (55.1)	746.3 (28.4)	686.3 (33.1)