A Mutualism Quantum Genetic Algorithm to Optimize the Flow Shop Scheduling with Pickup and Delivery Considerations

Table 8

Computational results of MQGA and QGA, MGA.

Problem	Approach		Improved%
N(FT06); (6 * 6)	MGA	103.3	—	73.3	13
	QGA	103.3	0	33.6	30
	MQGA	103	0.48%	9.8	113
N(FT10); (10 * 10)	MGA	1282.2	—	300.8	29
	QGA	1280.7	0.12%	97.3	98
	MQGA	1273.6	0.31%	76.2	163
N(FT20); (20 * 5)	MGA	1330	—	26.7	23
	QGA	1328.6	0.09%	18.3	36
	MQGA	1323.1	0.32%	36.7	133
N(ABZ3); (10 * 10)	MGA	2088.6	—	430.8	29
	QGA	2079.4	0.44%	146.6	101
	MQGA	2071.3	0.83%	83.4	164
N(ABZ6); (10 * 10)	MGA	1883.2	—	363.6	29
	QGA	1874.4	0.47%	111.3	101
	MQGA	1861.4	1.16%	123.3	164
N(ABZ7); (15 * 20)	MGA	1276.2	—	140.3	68
	QGA	1233.8	3.32%	104.3	172
	MQGA	1228.2	3.76%	86.8	310
N(ABZ8); (15 * 20)	MGA	1290.3	—	114.4	68
	QGA	1277.2	1.03%	17.7	172
	MQGA	1268.4	1.71%	19.8	310
N(ABZ9); (15 * 20)	MGA	1273.3	—	143.9	68
	QGA	1238.3	1.18%	29.7	171
	MQGA	1241.3	2.3%	11.3	310
N(TA1); (50 * 5)	MGA	3286.2	—	71.7	84
	QGA	3173.3	3.37%	17	426
	MQGA	3134.2	4.02%	21.1	778
N(TA2); (50 * 10)	MGA	3346.6	—	91.2	130
	QGA	4917	11.33%	9.1	480
	MQGA	4803	13.4%	19.3	814
N(TA3); (100 * 5)	MGA	10706.3	—	63.3	171
	QGA	9289.33	13.23%	16.2	632
	MQGA	9126.4	14.76%	17.8	963

Note: represents the converged generation and represents the average computation time.