# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 1 Introduction  
In recent years, distributed technologies have been widely adopted, with blockchain emerging as a prominent example of distributed database technology [1]. Blockchain is renowned for its characteristics of decentralization, immutability, transparency, and anonymity, providing a reliable framework for establishing trust within distributed systems. Initially, blockchain was primarily used to support cryptocurrency transactions and ledger functionalities. Over time, with technological advancements, blockchain has been applied across various domains, including finance [2], supply chain management [3] mechanical engineering [4], and the Internet of Things (IoT) [5]. The reliability of the blockchain system is highly dependent on the design of the consensus mechanism, a technology that enables efficient transaction verification by coordinating the protocols among network nodes and is crucial for maintaining data consistency and operational integrity. In the coalition chain scenario, algorithms such as PBFT,FBFT, DPoS, PoA, and Kafka constitute the mainstream solutions, and their degree of decentralization presents significant differences. Specifically, as follows: (1) Permission-based algorithms: the PoA mechanism [6] adopts a pre-selected authorization node model, and although its centralized architecture improves processing efficiency, there is a risk of single-point failure, and the failure of an authorization node may lead to system paralysis. Similarly, Kafka consensus [7] needs to rely on external message queues, introducing an additional operation and maintenance complexity and potential failure chains; (2) the delegated verification mechanism: DPoS [8] achieves transaction confirmation by electing representative nodes, although it is widely used in coalition chains. The failure of delegated nodes to perform their duties or malicious behavior may trigger a systematic trust crisis; (3) the fault-tolerant balancing scheme: PBFT, in contrast, builds a fault-tolerant framework through a three-phase communication protocol, which maintains network consistency even in the case of a 1/3 node failure. This balancing advantage enables it to gain a widespread application in scenarios such as supply chain traceability and cross-organizational data collaboration, supporting full-process automated validation from production traceability to delivery auditing, significantly enhancing the auditability and execution efficiency of the business chain. The blockchain transaction on-chain process is shown in Figure 1.  
Current reviews of blockchain consensus algorithms often exhibit a generalized breadth-oriented approach, primarily focusing on horizontal comparisons and analyzes of advantages and disadvantages across different algorithms. However, there has been a limited in-depth exploration of the evolutionary trajectory, optimization strategies, and applicability of individual consensus algorithms. Taking the PBFT consensus algorithm as an example, PBFT serves as a core consensus algorithm in consortium blockchain scenarios and has undergone multi-layered improvements in recent years. However, existing studies have extensively discussed improvements to the PBFT consensus, but they have yet to systematically classify the optimization methods of a PBFT consensus across multiple dimensions, nor have they provided a comprehensive evaluation of its network models, security, and applicability. Figure 1: A blockchain transaction on-chain schematic diagram. This paper focuses on the evolutionary trajectory of PBFT improvement algorithms, analyzing three key technical directions: communication complexity optimization, dynamic node management, and incentive mechanism integration. Specifically, it explores the following:  
1. Strategies for reducing communication overhead, and leveraging hierarchical networking and message compression;
2. Flexible architecture designs that support dynamic node admission and reputation evaluation;
3. Optimized view-change mechanisms based on a multi-leader node selection approach;
4.