A Multiobjective Optimization for Train Routing at the High-Speed Railway Station Based on Tabu Search Algorithm

Table 4

Pseudocode of the tabu search algorithm.

---

Input: as an initial solution, parameter , etc

assign the incumbent solution:

while the frequency of one solution is not reached:

generate neighborhood

update by removing duplicate and solutions that do not satisfy constraints

for i in :

get solutions: S(i) satisfied the constraints in the mathematical model

.append(S(i)) which is neighborhood solution

get the decision variable and correspondingly

choose the non-tabu optimal solution in

search the corresponding routing set:

if len()>5:

del

if the number of iterations is an integer multiple of 5:

if exist a solution satisfy aspiration criterion:

if len()>5:

del

Output: optimal solution: , and

---