Research Article
Comparative Study of Swarm-Based Algorithms for Location-Allocation Optimization of Express Depots
Table 2
Parameter settings for PSO, DE, ABC, and LDES algorithms.
| Parameter description | Value |
| General parameters | The maximum number of cost function evaluations | 200,000 | Search space | | Transportation cost weight | 1 | Setup cost weight | 1 | Operational cost weight | 0.1 | Punishment weight | 30 | Polynomial coefficients for operational cost | | Coordinate bounds | | PSO parameters (Bao et al. [6]) | Social learning factor | 2 | Personal learning factor | 2 | Weight range [] | [0.4,0.9] | Population size nPop | 30 | Velocity range [] | [−0.2,0.2] | DE parameters (Moonsri et al. [5]) | Population size nPop | 37 | Crossover probability | 0.9455 | Scaling factor | 0.6497 | ABC parameters (Guo and Zhang [3]) | Population size | 50 | Number of onlooker bees | 25 | Number of employed bees | 25 | Food source visiting limit | 500 | LDES parameters (Awad et al. [27]) | Initial population size | 360 | Minimum population size | 4 | Covariance matrix learning probability pc | 0.4 | Selection probability ps | 0.5 | Selection rate of the best solutions p_best_rate | 0.11 | Archive rate arc_rate | 1.4 | Memory size | 4 | The initial number of neighbors SEL | 180 | Scaling factors | Adaptive adjust |
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