In recent years, Blockchain Technology has become a paradigm shift, providing Transparent, Secure, and Decentralized platforms for diverse applications, ranging from Cryptocurrency to supply chain management. Nevertheless, the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency, scalability, and energy consumption. This study proposes an innovative approach to Blockchain network optimization, drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms. Specifically, we explore the application of genetic algorithms, particle swarm optimization, and related evolutionary techniques to enhance the performance of blockchain networks. The proposed methodologies aim to optimize consensus mechanisms, improve transaction throughput, and reduce resource consumption. Through extensive simulations and real-world experiments, our findings demonstrate significant improvements in network efficiency, scalability, and stability. This research offers a thorough analysis of existing optimization techniques, introduces novel strategies, and assesses their efficacy based on empirical outputs.

The integrity risks posed by data outsourcing in cloud storage have driven the development of remote data integrity auditing (RDIA) technologies. However, traditional schemes rely on trusted third-party auditors (TPAs), leading to potential collusion and single-point failure vulnerabilities. The integration of blockchain alleviates these issues through decentralization and transparency, yet existing blockchain-based certificateless auditing schemes still suffer from security flaws in the tag generation phase. Addressing the tag forgery vulnerability in Miao et al.’s scheme, which stems from the absence of random parameters in the hash function input, this paper proposes a lightweight enhancement mechanism: incorporating a random factor into the hash input during tag generation to ensure dynamic unforgeability of tags. While retaining the efficiency advantages of the original framework, the improved scheme achieves resistance against tag forgery, proof forgery, and collusion attacks under the Computational Diffie-Hellman (CDH) and Discrete Logarithm (DL) hardness assumptions, validated through rigorous formal proofs. Experimental performance analysis demonstrates that the proposed enhanced scheme introduces negligible computational overhead, providing a secure, practical, and transparent auditing solution for multi-cloud storage environments.