A Multi-Strategy Cooperative Dung Beetle Optimization Algorithm for Joint Resource Scheduling in MEC-Enabled Wireless Blockchain Networks

In mining on wireless blockchain networks that support mobile edge computing (MEC), resource scheduling methods are used to maximize mining profits. However, traditional scheduling approaches often fail to allocate resources efficiently, leading to low mining efficiency within these networks. To address this challenge, we propose a multi-strategy cooperative dung beetle optimization algorithm (MSCDBO) designed to adaptively optimize joint resource scheduling for mining decision and resource allocation in MEC-enabled wireless blockchain networks. In joint resource scheduling, mining decision is achieved by adjusting the population size (the number of miners), while resource allocation is performed by updating the population individuals (the participating miners). In the proposed algorithm, a chaos mapping reverse learning strategy is designed to improve the quality of the initial population, while a local mutation strategy based on adaptiveT-distribution and Lévy flight is developed to enhance the mining performance. Furthermore, a Tabu search strategy is incorporated to achieve optimal mining profits. A synthetic example result shows that compared with five competitive algorithms (CMA-ES, CTCM, HHO, DE, and DBO), the MSCDBO algorithm improve the overall profit by 5.56%, which verifies the effectiveness of the proposed approach for resource scheduling in MEC-enabled wireless blockchain networks.