Computing first access server deployment algorithm based on industrial computing first network
To solve the problem of low efficiency in industrial computing first network construction caused by the contradiction between the limited resources of computing first accessing servers and the instability of the system structure of computing power resources,a new quantum genetic algorithm was proposed.Based on the multi-objec-tive optimization model,the deployment of computing first access servers was analyzed.Subsequently,the ε-con-straint method was used to transform the problem into a computing first access server deployment problem based on the constraint method,and it was theoretically proved that the two problems were equivalent to some extent.When designing the algorithm,quantum gene loci were used to replace traditional gene loci to construct quantum chromo-somes.Afterwards,the entire search space was divided based on the constraints of the problem,and the population was evenly distributed through the size relationship between the probability amplitudes of quantum gene loci in the quantum chromosome.Based on the optimal quantum chromosome,the remaining quantum population was iterative-ly updated based on individual equilibrium and through quantum rotation gates.For each selected quantum chromo-some in the constraint space,a random quantum non gate was used to complete the mutation operation.The experi-ment verified the effectiveness and convergence of the algorithm from four aspects:the total error rate of computing resources,the load balancing deviation rate,the algorithm convergence rate and the optimal solution deviation rate.
industrial computing first networkcomputing first access serverε-constraint methodquantum genetic algorithm
万方数据
维普
