A Hybrid Genetic Algorithm for the Multiple Crossdocks Problem

Algorithm 1

HGA to solve problem (P).

Generate initial solutions by two-stage greedy method.

for 𝑖𝑡𝑒𝑟←1 to #maximum_iter  do

 for 𝑜𝑓𝑓←1 to #crossover do

  Randomly select Parent 1 and Parent 2.

  Crossover Parent 1 and Parent 2 to produce a new Offspring.

 end for

 for each offspring do

  Mutate offspring with individual mutation probability 𝑃_𝑖𝑚 and gene mutation probability

𝑃_𝑔𝑚

  Apply neighborhood search to each newly-produced Offspring.

 end for

 Select the best #𝑝𝑜𝑝𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑠 from all the Individuals including all current parents and

 newly produced offsprings.

 Update current best solution.

 if the best solution could not improve within #𝑡𝑒𝑟𝑚𝑖𝑛𝑎𝑡𝑒_𝑖𝑡𝑒𝑟 then

  Consider the solution as the goal optimal solution.

  break

 end if

end for

output the best solution and escaped time