A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

Table 8

Comparison between MOMAD and other algorithms using HV metric for Kacem and BR data instances.

Instance	Algorithm
	MOMAD	MOGA	PLS	HSFLA	HMOEA	SEA	P-EDA	hDPSO	PRMOTS + IS
ka		0.5777		—				—
ka		0.3385			0.3385
ka		—		—				—
ka		0.6560
ka		0.2739						0.7310
MK01	1.2130	1.0833	1.1820	0.7579	1.2003	1.2110	1.2022	1.1680
MK02		0.9152	0.9173	0.8032	0.9538	0.9402	0.9586		0.9370
MK03		0.6296	0.5443	0.5443	0.6574		0.6405	0.6331
MK04		1.0882	—	0.9804	—	1.1084	1.0893	1.0990	1.1044
MK05	0.8274	0.7805	—	0.8234	—	0.8274		0.7912	0.8131
MK06	0.9018	0.8163	—	0.7816	—	0.8766	0.8121	0.8307
MK07		0.7863	—	0.1805	—	0.1804		0.1900	0.1894
MK08		0.4469	0.4819	0.4819		0.5451		0.5375
MK09		1.2575	—	1.2742	—	1.2858	1.2939	1.3013	1.2897
MK10		0.8106	—	0.9372	—	0.9496	0.9369	0.7976	0.9652

For each instance, the greater HV values obtained by the compared algorithms are marked in bold.