Synchronous Optimization for Demand-Driven Train Operation Plan in Rail Transit Network Using Nondominated Sorting Coevolutionary Memetic Algorithm

Table 1

Pseudo code of NSCMA.

	Nondominated sorting coevolutionary memetic algorithm
1	Start
2	Number of generations for final termination: ;
3	Number of generations for early termination: ;
4	Number of generations for operator scoring: ;
5	Size of population: ;
6	Representative weights of passengers’ total time: ;
7	Probabilities of operators: , , ;
8	Tolerance factor of reference point: ;
9	Objective values of benchmark individual: , ;
10	Index of current generation: ;
11	Population initialization ();
12	Local search (, );
13	Nondominated sorting ();
14	Local sorting (, , , );
15	While do
16	Tournament selection ();
17	Crossover and mutation (, , , );
18	Population combination (, );
19	Local search (, );
20	Nondominated sorting ();
21	Local sorting (, , , );
22	Population replacement ();
23	POSS extraction ();
24	, , Operator scoring (, , );
25	Ifthen
26	Break;
27	End if
28	;
29	End while
30	Return ;
31	End