An important feature of blockchain technology is that all participants jointly maintain transaction data and can achieve mutual trust relationships without integrated control, which relies on distributed consensus algorithms. Practical Byzantine Fault Tolerant algorithm (PBFT) is a fault-tolerant algorithm based on state machine replication, which solves the Byzantine error, that is, the malicious behavior of nodes. In PBFT, all participating nodes are divided into the primary node and backup nodes. When this primary node commits evil or fails, it will elect a primary node again for message communication. The genetic algorithm (GA) is a computer simulation study inspired by the natural biological genetic evolution criterion “natural selection, survival of the fittest”. Genetic algorithm is actually a method to find the optimal solution. According to it, the best primary node is selected in the PBFT algorithm to improve consensus efficiency. The consensus algorithm is the guarantee of the decentralization feature in blockchain technology. The PBFT algorithm is a commonly used consensus algorithm. However, this algorithm has the following problems: when the primary node fails, it must be selected again, which leads to a decrease in consensus efficiency. This paper proposes a consensus mechanism based on an improved genetic algorithm, which uses an improved genetic algorithm to select the primary node. According to the genetic algorithm, the best primary node is selected, and it meets the minimum number of errors or evils and the highest transaction efficiency with other backup nodes. The improved consensus algorithm can effectively reduce system delay and improve consensus efficiency.
Cite this paper
Yang, C. , Wang, T. and Wang, K. (2020). A Consensus Mechanism Based on an Improved Genetic Algorithm. Open Access Library Journal, 7, e6713. doi: http://dx.doi.org/10.4236/oalib.1106713.
Kapitza, R., Behl, J., Cachin, C., Distler, T., Kuhnle, S., Mohammadi, S.V., Schr¨oder-Preikschat, W. and Stengel, K. (2012) Cheapbft: Resource-Efficient Byzantine Fault Tolerance. Proceedings of the 7th ACM European Conference on Computer Systems, April 2012, 295-308.
https://doi.org/10.1145/2168836.2168866
Wang, F.Y., Cai, S.S., Lin, T.C., et al. (2019) Study of Blockchains’s Consensus Mechanism Based on Credit. IEEE Access, PP(99), 1-1.
https://doi.org/10.1109/ACCESS.2019.2891065
Jeon, S., Doh, I. and Chae, K. (2018) RMBC: Randomized Mesh Blockchain Using DBFT Consensus Algorithm. 2018 International Conference on Information Networking (ICOIN), Chiang Mai, 10-12 January 2018.
https://doi.org/10.1109/ICOIN.2018.8343211
Castro, M. and Liskov, B. (2002) Practical Byzantine Fault Tolerance. ACM Transactions on Computer Systems, 20, 398-461. https://doi.org/10.1145/571637.571640
Cai, Q., Lin, J., Li, F., et al. (2014) EFS: Efficient and Fault-Scalable Byzantine Fault Tolerant Systems against Faulty Clients. International Conference on Security and Privacy in Communication Networks: 10th International ICST Conference, SecureComm 2014, Beijing, 24-26 September 2014, Revised Selected Papers, Part I 305-322. https://doi.org/10.1007/978-3-319-23829-6_22
Ardjmand, E., et al. (2020) A Hybrid Artificial Neural Network, Genetic Algorithm and Column Generation Heuristic for Minimizing Makespan in Manual Order Picking Operations. Expert Systems with Applications, 159, 113566.
https://doi.org/10.1016/j.eswa.2020.113566
Veeramsetty, V., Lakshmi, G.V.N. and Jayalaxmi, A. (2012) Optimal Allocation and Contingency Analysis of Embedded Generation Deployment in Distribution Network Using Genetic Algorithm. 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET), Kumaracoil, 21-22 March 2012.
https://doi.org/10.1109/ICCEET.2012.6203763
