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