Hybrid Differential Evolution-Particle Swarm Optimization Algorithm for Multiobjective Urban Transit Network Design Problem with Homogeneous Buses

Table 7

The Pareto front obtained by the hybrid DE-PSO.

Solution	Bus lines	Passenger cost (Cp)	Passenger cost above lower bound	Fleet size (Co)	d0	d1	dun	Maximum route headway (min)	Average route headway (min)	Average in-vehicle travel time (min)	Average passenger cost (min)	Average waiting time (min)
1	8	184,841	29,051	89	96.59	3.41	0.00	11.34	6.300	10.18	11.87	1.26
2	10	190,003	34,213	82	97.17	2.83	0.00	12.30	8.488	10.24	12.20	1.31
3	6	190,732	34,942	80	95.12	4.88	0.00	8.00	5.915	10.48	12.25	1.35
4	7	192,581	36,791	79	95.18	4.82	0.00	10.99	7.136	10.62	12.37	1.39
5	9	193,115	37,325	78	98.20	1.20	0.00	10.50	6.812	10.79	11.40	1.21
6	6	193,456	37,666	76	94.99	5.01	0.00	7.71	6.003	10.80	12.43	1.43
7	7	194,898	39,108	75	97.75	2.25	0.00	12.00	7.493	10.84	12.52	1.44
8	7	195,359	39,569	73	95.05	4.95	0.00	12.32	8.830	10.88	12.55	1.45
9	6	196,365	40,575	71	93.38	6.62	0.00	8.00	6.177	10.97	12.61	1.47
10	5	198,027	42,237	68	92.94	7.06	0.00	8.00	5.724	11.02	12.72	1.48
11	5	200,940	45,150	67	94.99	5.01	0.00	8.00	6.160	11.05	12.91	1.49