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<p>Blockchain scalability is a pressing challenge affecting blockchain throughput, latency, and energy consumption. This study proposes a dynamic block size optimization framework for private blockchain networks using hybrid heuristic algorithms—Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO)—integrated with Merkle trees and Directed Acyclic Graphs (DAGs). The core contribution is a novel block size adjustment strategy that relocates the Merkle root from the block header to a local buffer, reducing header size from 80 to 48 bytes and enabling higher transaction capacity. The framework achieved a 33.33% increase in transactions per block, a 30% reduction in latency, a 25% reduction in energy consumption, and a 60% improvement in transactions per kilobyte (KB). These results were validated using Python-based simulations, Ethereum transaction datasets, and defined experimental settings. The proposed solution is currently applicable to private blockchains, with future validation planned for public blockchain networks.</p>

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Dynamic strategy for adaptive block size optimization in blockchain technology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月20日

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  • Shafique Ahmed Awan,
  • Muazzam Ali Khan Khattak,
  • Anwar Ali Sathio,
  • Haleem Farman,
  • Sumaira Memon,
  • Moustafa M. Nasralla,
  • Ahmed Sedik

摘要

Blockchain scalability is a pressing challenge affecting blockchain throughput, latency, and energy consumption. This study proposes a dynamic block size optimization framework for private blockchain networks using hybrid heuristic algorithms—Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO)—integrated with Merkle trees and Directed Acyclic Graphs (DAGs). The core contribution is a novel block size adjustment strategy that relocates the Merkle root from the block header to a local buffer, reducing header size from 80 to 48 bytes and enabling higher transaction capacity. The framework achieved a 33.33% increase in transactions per block, a 30% reduction in latency, a 25% reduction in energy consumption, and a 60% improvement in transactions per kilobyte (KB). These results were validated using Python-based simulations, Ethereum transaction datasets, and defined experimental settings. The proposed solution is currently applicable to private blockchains, with future validation planned for public blockchain networks.