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$Q$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.

With the rise of cryptocurrencies, blockchain, which is the underlying technology of them, has gained more attention and been used in the Internet of Things (IoT) and other fields. However, there are bottlenecks that hinder its application, such as the storage capacity. Due to the large number of IoT devices which always act as data generators in many systems, the transactions will be generated at a high rate. The storage problem will be more serious in IoT. In this article, to expand the capacity of blockchain, for each peer, we select old blocks which are created previously and less likely to be queried and store them in the cloud. Based on this idea, we develop objective functions related to query probability, storage cost, and local space occupancy, which naturally narrows down the problem to block selection. The results can be obtained by solving a multiobjective optimization problem. We design a nondominated sorting genetic algorithm with clustering (NSGA-C), which changes the method of selecting individuals from the critical dominance layer by adding clustering to ensure diversity. A suitable solution can be selected from the Pareto set to fulfil the requirements of different users. We then compare the algorithm with the improved NSGA-II and NSGA-III, which both add integer constraints to the decision variables. The results show that our method is better than NSGA-II and NSGA-III in terms of local space occupancy in the blockchain application. In addition, it can also effectively avoid the risk of block overflow.