Jointly optimize energy consumption and load distribution in data transmission

To address the problem of high network energy consumption and imbalanced load distribution in data transmission, this paper proposes a novel deep reinforcement learning algorithm to jointly optimize energy consumption and link load distribution. We design a three-layer back-propagation neural network. It iteratively adjusts weights based on past states and actions, and minimizes the loss to improve action prediction accuracy. It guides the agent to make reasonable routing decisions within complex network environments. Based on this, we leverage Q-learning to seek paths for transmission demands. By dynamically aggregating and balancing traffic, energy efficiency and load balance can be achieved. We design reward functions from the viewpoint of link and node, respectively, for different optimization objectives. In order to obtain high efficiency and good robustness, we improve roulette-based Chebyshev scalarization function to solve the weight selection problem among multi-objectives. We update the Pareto set via multiple state transitions to approximate optimal solution. We use the Euclidean distance to measure optimizing effect of both objectives. Simulation results illustrate that our algorithm can effectively reduce network energy consumption and balance load distribution.