Swarm intelligence (SI) emerges as a highly promising domain for researchers in numerical optimization. Drawing from the collective intelligent behaviors of creatures such as honeybees, ants, fish, and birds, researchers have designed numerous swarm intelligence-based algorithms. SI-based algorithms find the near-optimal solution using the collective search pattern involving several search agents. Foraging agents update their positions to get the optimal solution using various position update processes. In SI-based algorithms, the position update process is significant in the solution search process. This article shows a detailed study of the role of the position update process in the performance of various swarm intelligence-based algorithms. It also details how the position update process influences the diversification and convergence abilities of the swarm in the solution search process.