| Procedure: transform the tasks to the gantry crane schedule and storage space allocation |
| Inputs: // is the total amount of storage spaces in main area, is the |
| earliest available time of gantry crane is the initial position of (denoted by the task |
| number), is the traveling time of crane between the adjacent tasks, is the safety |
| distance required between the adjacent gantry cranes// |
| Output: // is the maximum completion time of all the unloading tasks of the container train// |
| begin |
| calculate max0 and sol0 //find a good initial solution// |
| let pop (popsize, dim, low, up) //// |
| let fitness () |
| set fitness(index) = max0, pop(index, :) = sol0 //using the initial solution to replace the worst |
| solution in the population// |
| set historical_pop = pop //historical_pop is swarm-memory of BSA// |
| while non_iter <= nonmax && epk <= epoch do //epoch is the maximum iteration counter// |
| if rand < rand, |
| historical_pop = pop; |
| end if |
| calculatehistorical_pop, and map; // is the scale factor// |
| if rand < rand, |
| for :popsize, |
| = randperm(dim); |
| map((1:ceil(DIM_RATEranddim))) = 1; |
| end for |
| else |
| for :popsize, |
| map(, randi(dim)) = 1 |
| end for |
| end if//implement the SELECTION-I process// |
| offsprings = pop + (map.). (historical_pop-pop) |
| offsprings = BoundaryControl(offsprings, low, up) //implement the MUTATION and |
| CROSSOVER process// |
| calculate fitnessoffsprings; |
| ind = fitnessoffsprings < fitnesspop; |
| fitnesspop (ind) = fitnessoffsprings(ind); |
| pop(ind, :) = offsprings(ind, :); |
| [best, ind] = min(fitnesspop); |
| if best < globalminimum |
| globalminimum = best |
| globalminimizer = pop(ind, :); |
| non_iter = 0; |
| else |
| non_iter = non_iter + 1; |
| end if//implement the SELECTON-II process// |
| best_iter = [best_iter globalminimum]; |
| epk = epk + 1; |
| end while |
| end |
