Optimizing Resource Allocation in Blockchain Networks Using Neural Genetic Algorithm

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.