LAUSR

Molecular communication (MC) is a modern communication paradigm inspired by biological mechanisms and systems. Due to the short range of molecular diffusion, MC systems necessitate a multi-hop diffusion-based network to transmit information. Finding the optimal routing path is one of the most critical challenges in MC. The main goal is to transfer information through the diffusion of molecules within an optimal state by detecting the shortest route and the proper relays. In this paper, finding the optimal routing path using a genetic algorithm (GA) is investigated in order to find the shortest and the most energy-efficient path. Our model intelligently plans the optimum trajectory between the transmitter (TX) and the receiver (RX) by identifying the appropriate relays both locally and globally. Our GA implementation uses a variable-length chromosome encoding to obtain the optimal path by selecting an appropriate fitness function. We also examine and compare the performance of the proposed algorithm with Dijkstra’s algorithm (DA), which is one of the deterministic algorithms. Finally, various simulations for different sizes of MC networks are performed to verify the accuracy of the proposed method. Our simulation results demonstrate that the presented GA offers an accurate routing path within an excellent time, even in large-sized environments.