Quality-aware multi-objective cloud manufacturing service composition optimization algorithm
To solve the difficult problem of weighing the weights of multiple targets in the cloud manufacturing serv-ice composition,as well as significantly improve the population diversity of the evolutionary algorithm in the solu-tion process and effectively balances the global and local search capabilities of the evolutionary algorithm,an evolu-tionary algorithm based on adaptive selection and reverse learning strategy was proposed,while optimizing the time,cost,reliability,availability and credibility.To shorten the time to solve the combined solution,the K-means meth-od was used to cluster the candidate services based on the quality of service,and the poorer services were elimina-ted.Then,the reverse learning strategy was used to improve the global search performance,and the global and lo-cal search capabilities of the algorithm were effectively balanced through selection and probability update strategies.The results of comparative experiments with four advanced algorithms showed that the proposed algorithm had bet-ter comprehensive performance.
service compositionservice qualitymulti-objective optimizationevolutionary algorithm
NETL
NSTL
万方数据
