The code defines a class called GA_PSO, which appears to be a hybrid optimization algorithm combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This code is part of a program that seems to be handling some kind of network optimization problem involving base stations and edge computing.
In the code:
1.	The class initializes parameters for both GA and PSO algorithms: 
GA parameters: crossover probability (pc), mutation probability (pm)
PSO parameters: inertia weight (w), learning coefficients (c1, c2)
General parameters: dimensions, gene length, population size, maximum iterations
2.	The class stores several data structures: 
population: the parent population of solutions
V: velocity vectors for PSO
new_popu: the new population after selection operations
new_V: updated velocity vectors
fitness: fitness values for all genes/solutions
elite: elite genes
pbest: personal best solutions
3.	 Network-specific variables: 
edge_num: number of edge nodes
tran_v: transmission speed (20 MB*8/s = Mbit/s)
bs_local: base station locations
edge_local: edge node locations
work_size_total: total work size
matrix, path, dis: matrices for distances/paths
tran_time: transmission time
edge_use: edge usage
4.	The read () method reads base station coordinates from an Excel file: 
It opens a file 
Reads base station coordinates
Stores them in bs_local

The GA_PSO class includes several key methods:
1.	read () - Loads data from Excel files including: 
Edge node locations
Task information
Network topology matrix
2.	Dijkstra () - Implements the Dijkstra algorithm to find shortest paths in the network
3.	init_Population() - Initializes the population for the genetic algorithm with random values
4.	init_task() - Prepares task data for processing
5.	max_min() - Helper function to find maximum and minimum values while handling infinity values
6.	com_fit() and com_fit1() - Calculate fitness values for the population and track best solutions
7.	com_parallel() - Core computation function that processes task distribution according to the gene representation
The algorithm appears to be solving a task allocation problem in an edge computing network, where tasks from base stations need to be distributed to edge servers while optimizing for path distances and workload.