基于改进萤火虫算法的分布式能源供应链配置研究

针对分布式能源供应链的配置问题,提出了改进人工萤火虫算法,结合云计算技术解决该配置问题。首先,以人工萤火虫算法的决策域半径为切入点,改进人工萤火虫算法的决策域半径,有效地解决了传统人工萤火虫算法寻优不稳定、算法精度低、后期收敛速度较慢的缺点;其次,全面采集系统信息,考虑各云处理中心各服务器的负载情况,建立基于改进人工萤火虫算法的分布式能源供应链配置需求侧均衡模型,以达到云计算环境下能源供应链中的配置均衡目标;最后,仿真分析表明,改进人工萤火虫算法可以更快、更稳定、更均衡地处理系统中的任务,优化分布式能源供应链配置。

Research on distributed energy supply chain configuration based on improved glowworm swarm optimization algorithm

Aiming at the configuration problem of distributed energy supply chain, an improved glowworm swarm optimization algorithm was proposed, which was combined with cloud computing technology to solve the configuration problem. Firstly, the radius of decision domain of glowworm swarm optimization algorithm was taken as the cut-in point to improve its radius of this algorithm, which could effectively solve the shortcomings of traditional glowworm swarm optimization algorithm such as unstable optimization, low accuracy, and slow convergence rate in the later stage. Secondly, the system information was collected comprehensively. And the load of servers in cloud processing centers was taken into accounts. The demand-side balanced model of distributed energy supply chain configuration was established based on the improved glowworm swarm optimization algorithm to achieve the goals of balanced allocation in the energy supply chain under cloud computing environment. Finally, the simulation results showed that the improved glowworm swarm optimization algorithm could deal with the tasks of distributed energy supply chain faster, more steadily, and in equilibrium, and thus optimize its allocation.