A variable-length encoding genetic algorithm for incremental service composition in uncertain environments for cloud manufacturing

Abstract

Service composition and optimal selection (SCOS) plays a crucial role in cloud manufacturing (CMfg). While the existing service composition methods are hard to address the changes and uncertainties of CMfg dynamic environment. Therefore, a variable-length encoding genetic algorithm for structure-varying incremental service composition (ISC-GA) is proposed in this paper. Specifically, a novel variable-length encoding scheme containing structural information is proposed to describe the uncertain and changing process model. And the improved crossover and mutation algorithm suitable for individuals with nonlinear varying structure and incremental service composition is designed. It is realized by optimizing both the process structure and service instance combinations, and overcomes the drawbacks resulted from single preset process structure. Due to the difficulty of fitness computation caused by uncertain process structures, novelty is introduced as a new evolutionary pressure, and a novel framework for ISC-GA is presented, which helps to find both novel and high-performance solutions. Experimental results indicate the effectiveness of the proposed approach.