The Internet of Data(IoD)is a new information infrastructure proposed to address low transmission efficiency,high coordination costs,and challenging security controls in current internet data resource sharing and interactions.The key to enabling data circulation lies in the identification resolution system.However,as identification registration and resolution rapidly increase,resolution nodes encounter numerous high-concurrency resolution requests,making it difficult to ensure service quality.To tackle this issue,we propose a novel approach,the adaptive Discrete Particle Swarm Optimization algorithm based on Genetic Algorithm(DPSO-GA),considering the node architecture's characteristics in IoD's identification resolution system.This method aims to identify layouts effectively to reduce resolution latency.It comprehensively considers factors such as bandwidth,identifier count,and node capacity's impacts on resolution latency between nodes.Additionally,it introduces crossover and mutation operations from a Genetic Algorithm(GA),employs an adaptive strategy for the inertial weight factor of the Particle Swarm Optimization(PSO)algorithm,and utilizes a linear increasing/decreasing strategy for the learning factor.Experimental results demonstrate that compared to traditional PSO and GA,DPSO-GA improves optimization by 48.9％and 19.9％,respectively.This enhances population evolution diversity,expands search range,and reduces algorithm time complexity and the likelihood of falling into a local optimum,thereby consistently reducing identification resolution delays.
关键词
数联网/标识解析/数据布局/启发式算法/遗传算法/粒子群优化算法
Key words
Internet of Data(IoD)/identifier resolution/data layout/heuristic algorithm/Genetic Algorithm(GA)/Particle Swarm Optimization(PSO)algorithm
维普
万方数据