Parameter Optimization for Molecular Communication via Diffusion Model using Equilibrium and Enhanced Equilibrium Algorithms

Molecular communication (MOC), which is proposed as a new communication method between nano-sized devices (nanomachines), holds the potential to be used for diagnosing and treating diseases such as Alzheimer’s, Spinal Cord, and Parkinson’s in the future. Several methods for analyzing MOC models can be found in the literature. However, many of them propose using constant transmitters and receivers in a diffusion environment. In contrast to the existing literature, this study proposes a MOC model that considers the transmitter, receiver, and molecule as mobile entities in the diffusion environment. Furthermore, the system’s performance has been improved by identifying optimal parameters, such as the diffusion coefficient that controls mobility and the receptor radius that directly affects the system’s performance, using the equilibrium optimization (EO) algorithm and enhanced equilibrium optimization \((E^2O)\) algorithms. All optimized results are presented with mean and standard deviation values, demonstrating the reproducibility and consistency of the findings. In conclusion, solving the stochastic MOC problem with deterministic approaches such as the EO and \(E^2O\) algorithms offers several advantages.