[Retracted] Study on the Reform and Development of Modern Physical Education Teaching Based on 5G Internet Communication Technology

Algorithm 1

Algorithm for glowworm swarm optimization.

	Step 1: Initialize glowworm swarm Glowworm number in swarm, step , fluorescein initial value , fluorescein volatilization rate , domain change rate , decision domain initial value domain threshold max, and other specifications required to be assigned in the initialization.
	Step2: Estimate glowworm fitness depending on the objective function. Identify the fitness of every glowworm at its position related to specific objective function .
	Step 3: Identify the movement of motion and step of the glowworm. Every glowworm looks for glowworms with higher fluorescein value within its decision radius and estimates the next movements, related to fluorescein distance and value.
	Step 4: Update glowworm locations. Update the position of each glowworm xi based on estimated moving direction and step.
	Step 5: Glowworm decision domain radius should be upgraded.
	Step 6: Determine whether the algorithm has converged or attained its higher number of iterations (itmax) and whether to proceed to the further round of iterations. By modifying the initial distribution of glowworm swarm, it can be learned that algorithm implementation can be enhanced, and the premature local optimum of an algorithm can be neglected.