The inherent chain structure of blockchain means that its data volume grows linearly and endlessly. Over time,it causes a lot of pressure on the storage of the single node,which greatly wastes the storage space of the whole system. The Simplified Payment Verification(SPV)node model proposed in the Bitcoin white paper greatly reduces the node's need for storage space. However,it reduces the number of nodes and increases the pressure,which weakens the decentralization of the entire system and has security risks such as denial of service attacks and witch attacks. By analyzing the Bitcoin block data,a fully functional enhanced lightweight node model Enhanced SPV(ESPV)was proposed. The block was divided into new blocks and old blocks by ESPV,and different storage management strategies were adopted for them. The new block was saved in full copy(one copy per node)for transaction verification,allowing ESPV to has transaction verification(mining) function with less storage space cost. The old block was stored in the nodes of the network in slices,and was accessed through the hierarchical block partition routing table,thereby reducing the waste of the storage space of the system under the premise of ensuring data availability and reliability. The ESPV nodes have full node functionality,thus ensuring the decentralization of the blockchain system and enhancing the security and stability of the system. The experimental results show that the ESPV nodes have more than 80% transaction verification rate,and the data volume and growth amount of these nodes are only 10% of those of all nodes. The data availability and reliability of ESPV are guaranteed,and it is applicable to the whole life cycle of the system.

In order to overcome the problems of low convergence precision and easily relapsing into local optimum in Fruit fly Optimization Algorithm (FOA), by introducing the Levy flight strategy into the FOA, an improved FOA called double subgroups FOA with the characteristics of Levy flight (LFOA) was proposed. Firstly, the fruit fly group was dynamically divided into two subgroups (advanced subgroup and drawback subgroup) whose centers separately were the best individual and the worst individual in contemporary group according to its own evolutionary level. Secondly, a global search was made for drawback subgroup with the guidance of the best individual, and a finely local search was made for advanced subgroup by doing Levy flight around the best individual, so that not only both the global and local search ability balanced, but also the occasionally long distance jump of Levy flight could be used to help the fruit fly jump out of local optimum. Finally, two subgroups exchange information by updating the overall optimum and recombining the subgroups. The experiment results of 6 typical functions show that the new method has the advantages of better global searching ability, faster convergence and more precise convergence.