Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Table 5

Comparison of different factors’ influences on service composition.

Algorithms	Optimization results	MDsum	SDsum	CEsum	ETsum	ECsum	Relative deviation
IPSOA	{S2,1, S1,2, S1,3, S2,4, S1,5, S3,6S2,7}	4.47	18.142	7.887	406	13671	0.160
MaxMDM	{S1,1, S1,2, S1,3, S3,4, S1,5, S3,6S2,7}	5.15	18.150	8.675	448	16089	0.284
MaxSDM	{S2,1, S1,2, S2,3, S3,4, S1,5, S3,6, S2,7}	4.62	19.035	8.409	426	14879	0.210
MinCEM	{S2,1, S2,2, S1,3, S2,4, S1,5, S1,6, S2,7}	3.77	15.919	7.317	431	15106	0.338
MinETM	{S2,1, S1,2, S1,3, S2,4, S1,5S3,6, S2,7}	4.47	18.142	7.887	406	13671	0.160
MinECM	{S2,1, S1,2, S1,3, S1,4, S1,5S3,6, S2,7}	4.62	16.443	7.921	418	13608	0.192