Research On Service Composition Optimization Methods For Large-Scale Multi Batch Task In Cloud Manufacturing

Cloud manufacturing is the application and derivation of cloud computing in the field of manufacturing.It extends the shared resources from computing resources to the whole manufacturing resources,and becomes an important part of modern intelligent manufacturing.With the continuous application of cloud manufacturing technology,the composition and optimization of cloud manufacturing resources has become the focus of the industry,and it is the key issue to solve the interconnection of resources and services in cloud manufacturing environment.According to the actual conditions of cloud manufacturing,large-scale multi batch task collaborative execution is a common and popular manufacturing method in cloud manufacturing,which can reduce manufacturing costs and shorten production time for enterprises.Therefore,the cloud manufacturing service composition problem of large-scale multi batch task collaborative execution has become the main research of cloud manufacturing service composition.This dissertation mainly focuses on the service composition problem of large-scale multi batch task collaborative execution in the cloud manufacturing environment,and takes the production time in cloud manufacturing as the optimization objective.The main work of this dissertation is as follows:1.Aiming at the actual situation of large-scale user demand and multi batch task execution in cloud manufacturing environment,a service composition model of large-scale multi batch task collaborative execution was established.This model combined the cloud manufacturing service composition problem,analyzed the logical hierarchy and relationship among cloud platform,task demander and service provider,defined the relevant parameters and evaluation indicators,and realized the unifiedencapsulation of complex and massive manufacturing resources.2.In order to solve the problem of weak global optimization ability of existing optimization algorithms in solving cloud manufacturing service composition,a novel optimization method named as Multiple Improvement Strategies based Artificial Bee Colony Algorithm was designed to solve the problem.MISABC improved the performance of classical ABC algorithm through several strategies such as(a)differential evolution strategy(DES),(b)oscillation strategy with classical trigonometric factor(TFOS),(c)different dimensional variation learning strategy(DDVLS),(d)Gaussian distribution strategy(GDS).Eight benchmark functions with different characteristics,and a case study were used to validate the performance of the algorithm.The results demonstrated the solutions obtained by the proposed algorithm have better quality and higher stability.3.Most of the mathematical models of cloud manufacturing resource composition and optimization show the characteristics of multimodality and non-separable.In order to solve the problem of slow convergence and easy to fall into local optimization of existing optimization algorithms,a novel optimization method named as Improved Hybrid Differential Evolution and Teaching Based Optimization was proposed.Based on the differential evolution algorithm and the teaching and learning based optimization algorithm,the proposed algorithm makes full use of the global optimization ability of differential evolution algorithm and the local search ability of teaching and learning based optimization algorithm,improved and combined the two algorithms,thus enhanced the convergence speed of the algorithm and the ability to jump out of the local minimum.Finally,the feasibility and efficiency of the algorithm in solving complex cloud manufacturing service composition problems are verified by the experiment of benchmark functions and a case study.