Table 2
Algorithm pseudo-code.
| Algorithm hybrid PSO-GA for FCCL warehouse location |
|---|
| Input: Candidate distribution centers, supplier and retailer data, cost parameters |
| Output: Optimal warehouse location and delivery routes |
| 1: Initialize parameters (population size N, crossover probability Pc, mutation probability Pm, inertia weight w, maximum iterations Tmax) |
| 2: Randomly generate the initial population and encode individuals |
| 3: Evaluate the fitness of each individual (based on total distribution cost objective function) |
| 4: While (iteration < Tmax) do |
| 5: Apply GA selection operator (roulette wheel + tournament) to retain high-fitness individuals |
| 6: Perform crossover operation to generate offspring |
| 7: Perform mutation operation to maintain diversity |
| 8: Integrate PSO mechanism: update velocity and position |
| 9: v(t+1) = w*v(t) + c1*r1*(pbest − x) + c2*r2*(gbest − x) |
| 10: x(t+1) = x(t) + v(t+1) |
| 11: Recalculate the fitness of all individuals |
| 12: Update personal best (pbest) and global best (gbest) |
| 13: Dynamically adjust inertia weight w (decreasing from 0.9 to 0.4) |
| 14: End while |
| 15: Return the global best solution (warehouse location and routing scheme |
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