Abstract
Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.
Highlights
In today’s world, the trend of manufacturing globalization, diversification of consumer demand, and shortening of product marketization cycle has brought great challenges to traditional manufacturing enterprises
We study the mathematical model of multiple influence factors in cloud manufacturing service composition and the service composition optimization algorithm
In order to solve the problems of low search efficiency and inaccurate optimization in existing service composition optimization methods, the multiobjective optimization of cloud manufacturing service composition is discussed, a new improved particle swarm algorithm is proposed, and comprehensive service quality evaluation method is studied
Summary
In today’s world, the trend of manufacturing globalization, diversification of consumer demand, and shortening of product marketization cycle has brought great challenges to traditional manufacturing enterprises. Cloud manufacturing is developed on the basis of cloud computing technology It integrates service-oriented technology, Internet technology, communication technology, modern logistics technology, Internet of ings technology, high-performance computing, and artificial intelligence technology to virtualize and servitize all kinds of manufacturing resources and manufacturing capabilities of resource providers, so as to achieve unified and centralized intelligent management and operation. It can provide timely, safe, high-quality, and low-cost cloud manufacturing services for resource users [3]. E remaining chapters of this paper are arranged as follows: Section 2 comprehensively analyzes the research work done by domestic and foreign scholars on cloud manufacturing service composition optimization; Section 3 gives the definitions and calculation methods of cloud entropy, service matching degree, composition synergy degree, execution time, and execution cost; Section 4 proposes the IPSOA algorithm; Section 5 analyzes and verifies the performance of the proposed optimization algorithm through application example; and Section 6 summarizes the whole paper and puts forward the future work
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