This thesis presents a swarm-based optimization method inspired by the overdamped Langevin dynamics. A swarm consists of particles, each characterized by their position and friction coefficient, and subjected to a random force. The particles communicate by updating their friction coefficients based on relative performance within the swarm. The friction communication mechanism enables better-performing particles to move slower and remain near optimal regions, while worse-performing particles move faster to explore the search space. This creates a balance between exploration of new areas and careful exploitation of promising regions. The communication mechanism leads to emergent annealing behaviour, where average friction increases over time, similar to temperature reduction in simulated annealing.