An Evolutionary Approach to Joint Latency and Reward Optimization for Block Verification in Blockchain Networks

Shoufei Han, Xiaojing Liu, MengChu Zhou, Kun Zhu, Liang Zhao, Changhe Li

Abstract: This work studies the problem of block validation in a blockchain network where a block manager acting as a task publisher sends a task (block validation) to all the workers (miners) within the network. The latter carries out the block validation and finally returns the final results to the former. The goal of this work is to maximize the block manager’s profit by jointly optimizing the latency of the block verification process and the reward offered by the manager to miners. Note that the latency and reward are closely coupled. Therefore, in this case, if it is solved directly, they are offered separately, and their dependency is not well considered, leading to overall poor performance. This work formalizes it as an optimization problem considering both delay and reward, and proposes an evolutionary approach, namely, the reborn dandelion algorithm (RDA), to solve it. Specifically, in the proposed algorithm, each individual contains both delays and rewards for different types of miners. A reborn strategy is designed to reborn an individual to replace the worst one in the current population, with the aim of enhancing its exploration ability. A greedy selection strategy is proposed to enhance its exploitation ability. The experimental results on CEC2013 functions and blockchain network instances indicate that our proposed approach is significantly superior to other evolutionary-based ones.

External IDs:dblp:journals/tsmc/HanLZZZL25