A Modified Biogeography Approach To Cloud Manufacturing Service Composition

The cloud manufacturing model is a new type of manufacturing model that can make full use of the company’s production resources and manufacturing capabilities in a shared manner,thereby helping small and medium-sized enterprises to cope with the difficulties of the deteriorating economic situation,rapid changes in market demand and rapid rise in personnel costs.With the rapid development of the Internet of Things,cloud computing,mobile computing,big data analysis and industrial technology,the cloud manufacturing model is gradually occupying an increasingly important position.Faced with the needs of different service objects with different scales and levels,how to choose the appropriate abstract service from the large number of manufacturing capabilities and resources provided by different service providers and combine it into the best manufacturing service set has become an urgent solution.The problem,the optimization of manufacturing Service composition(MSC),is thus raised.When the similar manufacturing services with different service qualities increase rapidly,it is usually an NP-hard problem to choose the best manufacturing service combination,and it is difficult to solve the large-scale MSC problem through traditional optimization methods.Fortunately,heuristic algorithms and intelligent algorithms are widely used to solve NP-hard problems,have good execution efficiency and optimization effects,and are an effective way to solve the optimization problem of manufacturing service composition.Therefore,this article will also introduce some intelligent Optimization algorithm to explore the optimization problem of manufacturing service composition.This thesis focuses on the optimization of cloud manufacturing service composition.First,this thesis proposes a service quality evaluation model that includes five optimization indicators of time,cost,throughput,reliability,and satisfaction;second,for the different service quality requirements and optimization preferences of users,this thesis introduces the application of AHP For the calculation of the objective function value,to accurately capture the user’s optimization preferences;then,a modified biogeography-based optimization algorithm(MBBO)is proposed to solve the optimal cloud manufacturing service composition solution,through a dynamic adjustment of mutation Mechanism to balance global and local optimality,maintain ideal habitat characteristics and increase population diversity.Finally,the MBBO algorithm is applied to the complex multi-objective optimization problem of cloud manufacturing service composition.Simulation experiments are performed by changing the number of tasks and the number of candidate services,and the results are compared with the original biogeography-based optimization algorithm and the mutation-free biogeography optimization algorithm.Compared with other heuristic methods such as ant colony algorithm,genetic algorithm,evolution strategy,differential evolution and particle swarm optimization.The statistical analysis of the experimental results shows that the modified biogeography-based optimization algorithm proposed in this thesis has better optimization performance than other comparison algorithms.Even in large-scale tasks and candidate services,the proposed method also shows obvious advantages.