LAUSR

As the underlying technology of cryptocurrencies, blockchain has gained a lot of attention in recent years. However, the storage problem needs to be solved with the increasing number of blocks in the blockchain network. Cloud storage optimization is an effective way to solve the storage issue, which selects and stores parts of blocks to the cloud. Precisely, block selection can be described as a multiobjective optimization problem (MOP) and solved by evolutionary algorithms (EAs). To obtain well results of block selection, an improved NSGA-III algorithm based on deep Q-networks (DQN), termed NSGA-DQN, is proposed in this paper, which aims to maintain well convergence and diversity of the population. This way, a set of suitable solutions is obtained to determine the number of blocks stored to the cloud, and the storage problem can be solved effectively. To be specific, DQN creates a decision-making agent to maximize the expected reward by learning a policy that evaluates $Q$Q values of each action in each state. In the proposed selection mechanism, the reward values are set according to the convergence and diversity of the population, and the actions correspond to the individuals. This way, our method can determine a set of individuals that maximizes the convergence and diversity of the population. In addition, an adaptive maximum reward enhancement module (AMREM) is developed to further enhance the maximum expected reward by updating the new better reward and modifying the replay memory. We conduct the experimental study on block selection, and the results demonstrate that the proposed algorithm is superior to five state-of-the-art algorithms.