Developing Programming Tools to Handle Traveling Salesman Problem by the Three Object-Oriented Languages

Algorithm 10

Our HGA’s pseudocodes.

1    Create population of random tours;

        //First storey is GA and increases tours’  quality. It uses IGX as its crossover [5].

2    Use steady-state GA by heuristic crossover to improve population;

3    Sort population according to cost in ascendant order;

4    Tour    best-so-far    population0;

        //Second storey is HGA and uses GSX as its crossover, Double-Bridge as its mutation and LK

        as its LS.

5    for (i = 1; i <= gen-size; i++)

6    {

7     Tour    child;

8     int index;

9     if(rand_01() < crossover-rate)

10  {

11    index = linear selection from population;

12    father    population[index];

13    index = linear selection from population;

14    mother    population[index];

15    crossover(father, mother, child);

16    Improve child by lk;

17  }

18  else

19  {

20    child    linear selection from population;

21    mutate child by double-bridge;

22    Improve child by lk;

23  }

24  Sort population according to cost in ascendant order;

25  if (i % period == 0)

26    update candidate set according to edges density in population;

(27)  if(best-so-far cost > population0  cost)

(28)    best-so-far cost    population0;

(29) }

(30) Report best-so-far;