The grasshopper optimization algorithm (GOA) is a novel metaheuristic algorithm. Because of its easy deployment and high accuracy, it is widely used in a variety of industrial scenarios and obtains good solution. But, at the same time, the GOA algorithm has some shortcomings: (1) original linear convergence parameter causes the processes of exploration and exploitation unbalanced; (2) unstable convergence speed; and (3) easy to fall into the local optimum. In this paper, we propose an enhanced grasshopper optimization algorithm (EGOA) using a nonlinear convergence parameter, niche mechanism, and the β-hill climbing technique to overcome the abovementioned shortcomings. In order to evaluate EGOA, we first select the benchmark set of GOA authors to test the performance improvement of EGOA compared to the basic GOA. The analysis includes exploration ability, exploitation ability, and convergence speed. Second, we select the novel CEC2019 benchmark set to test the optimization ability of EGOA in complex problems. According to the analysis of the results of the algorithms in two benchmark sets, it can be found that EGOA performs better than the other five metaheuristic algorithms. In order to further evaluate EGOA, we also apply EGOA to the engineering problem, such as the bin packing problem. We test EGOA and five other metaheuristic algorithms in SchWae2 instance. After analyzing the test results by the Friedman test, we can find that the performance of EGOA is better than other algorithms in bin packing problems.

Blockchain technology is a unique distributed ledger technology that has been widely used in various areas. With the increase in data on the blockchain and the append-only nature of the blockchain, the traditional blockchain’s full replica storage technique leads to blockchain storage scalability problem. Existing methods prioritize minimizing the storage strain on blockchain nodes while ignoring the availability of data, resulting in a lengthy average response time for users to access the blockchain. To address the shortcomings, this paper proposes an Information-Centric Networking-based blockchain storage architecture. The architecture uses the enhanced resolution system for community division to build blockchain node partitions and store blockchain ledgers in the underlying network. It introduces virtual chain for rapid blockchain indexing and adopts a collaborative block replica deletion algorithm across neighboring partitions, including replica number decision based on blockchain access decay characteristics and replica deletion based on resource relationship. Finally, we compare and analyze the proposed blockchain storage architecture with BC-store and KASARASA, and the results demonstrate that this architecture has significantly lower average access time than others. The replica data volume of this method is reduced by 57.2% compared to the full replica policy, but the access time is only 5.2% slower when compared to the full replica policy, which substantially increases the replica storage utilization.

In the era of the digital economy, blockchain has developed well in various fields, such as finance and digital copyright, due to its unique decentralization and traceability characteristics. However, blockchain gradually exposes the storage problem, and the current blockchain stores the block data in third-party storage systems to reduce the node storage pressure. The new blockchain storage method brings the blockchain transaction retrieval problem. The problem is that when unable to locate the block containing this transaction, the user must fetch the entire blockchain ledger data from the third-party storage system, resulting in huge communication overhead. For this problem, we exploit the semi-structured data in the blockchain and extract the universal blockchain transaction characteristics, such as account address and time. Then we establish a blockchain transaction retrieval system. Responding to the lacking efficient retrieval data structure, we propose a scalable secondary search data structure BB+ tree for account address and introduce the I2B+ tree for time. Finally, we analyze the proposed scheme’s performance through experiments. The experiment results prove that our system is superior to the existing methods in single-feature retrieval, concurrent retrieval, and multi-feature hybrid retrieval. The retrieval time under single feature retrieval is reduced by 40.54%, and the retrieval time is decreased by 43.16% under the multi-feature hybrid retrieval. It has better stability in different block sizes and concurrent retrieval scales.

Multi-board electronic cases with high-density and power modules are widely used in industrial power supply management. Heat dissipation becomes an important factor in the design process in improving case performance and miniaturization requirements. The design of existing small electronic thermal methods ignores high-temperature and high-load environment tests without automation control. To solve these problems, a heat dissipation case is designed with a magnesium and aluminum alloy, for intelligent temperature control based on a high-power and high-density power supply array. Based on the extreme miniaturization design principle, a composite heat dissipation mode is adopted based on conduction and supplemented by forced air cooling. The results show that the heat dissipation design in this article can work normally in high-temperature and high-load environment tests. Finally, the existing cooling designs are compared and analyzed. The cooling performance parameters in this article are better than those in the existing case. It contributes to the thermal design of miniaturized electronic cases in power supply management.