# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 4 Analysis of the Improved PBFT Consensus Algorithm
### The Hierarchical Group Consensus Algorithm
During each consensus round, the reputation scores of nodes are updated based on their responsiveness, past reliability, and the precision of transaction consensus. Nodes that exhibit a higher credibility are chosen as leaders to decrease the chances of malicious leaders emerging. [PERSON] et al. [94] introduced an enhanced reputation-based Practical Byzantine Fault Tolerance consensus algorithm (RPBFT) that integrates node grouping and reputation-driven voting. Initially, the RPBFT algorithm optimizes the consistency process by leveraging the response speed of nodes within each group as a criterion for grouping, which is succeeded by achieving consensus within the group. The leader node within each group obtains the consensus result and participates in confirming consensus outside the group, reducing both communication overhead and latency between nodes. Furthermore, the RPBFT algorithm introduces a reputation model and voting mechanism, where the leader node of each group is elected based on reputation-driven voting, thereby enhancing the credibility and reliability of trusted nodes and preventing leader node failure. [PERSON] et al. [95] have put forward the Dual-Layer Byzantine Fault Tolerance (DLBFT) mechanism, which aims to significantly boost the scalability of blockchain technology while simultaneously enhancing the efficiency of information exchange within Building Information Modeling (BIM). This innovative study presents a dual-layer network architecture that is designed to optimize communication and data flow. In this architecture, each node in the first layer is interconnected with multiple nodes in the second layer, creating a well-defined and structured grouping of nodes. The framework utilizes the Byzantine Fault Tolerance (BFT) algorithm, which plays a crucial role in facilitating consensus among nodes. This algorithm is pivotal in ensuring that information is exchanged securely and efficiently across the network. By implementing DLBFT, the researchers aim to address the challenges related to scalability in blockchain systems while maintaining the integrity and reliability of data exchange in BIM contexts. [PERSON] et al. [96] proposed ST-PBFT, a partitioning method based on consistent hashing, designed to reduce communication complexity in IP transaction scenarios. Furthermore, [PERSON] et al. [86] introduced GV-PBFT, a PBFT consensus optimization algorithm based on group reputation values, aimed at enhancing system efficiency. [PERSON] et al. [97] introduced the Grouped Multilayer PBFT (GM-PBFT) consensus algorithm, specifically optimized for digital asset trading scenarios, aiming to mitigate communication complexity and enhance consensus efficiency. Overall, these studies highlight continuous advancements in PBFT consensus mechanisms, leveraging techniques such as node grouping, reputation-based selection, and algorithmic optimizations to improve scalability, communication efficiency, and fault tolerance in blockchain systems. As shown in Table 10, this section provides an explanation of the grouping methods used for the algorithms introduced. These grouped consensus algorithms, while ensuring consensus efficiency, have also made significant improvements in fault tolerance in some cases. The primary reason for this improvement is that, by grouping nodes within the network, these algorithms allow each group to tolerate up to f Byzantine nodes (where N = 3f + 1), while also tolerating f Byzantine nodes outside the group. Therefore, from an overall perspective, these algorithms can tolerate nearly half of the Byzantine nodes within the network. The optimization algorithms, however, exhibit challenges such as complex inter-group coordination and the risk of load imbalance. They are suitable for scenarios with concentrated node geographic locations and stable network topologies (e.g., supply chain traceability systems), but perform poorly in highly dynamic wireless networks, such as vehicular networks (VANETs).