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Open Access

ARTICLE

Optimizing Resource Allocation in Blockchain Networks Using Neural Genetic Algorithm

Malvinder Singh Bali1, Weiwei Jiang2,*, Saurav Verma3, Kanwalpreet Kour4, Ashwini Rao3
1 Department of Data Science & Engineering, Manipal University Jaipur, Jaipur, 303007, India
2 School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China
3 SVKM’s NMIMS Mukesh Patel School of Technology Management and Engineering, Mumbai, 400056, India
4 School of Computer Science & Engineering, Manipal University Jaipur, Jaipur, 303007, India
* Corresponding Author: Weiwei Jiang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.070866

Received 25 July 2025; Accepted 01 October 2025; Published online 03 November 2025

Abstract

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.

Keywords

Blockchain technology; energy efficiency; environmental impact; evolutionary algorithms; optimization

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