A service composition method using improved hybrid teaching learning optimization algorithm in cloud manufacturing

In the cloud manufacturing process, service composition can combine a single service into a complex service to meet the task requirements. An efficient service composition strategy is crucial, as it affects the efficiency of resource and capacity sharing in the cloud manufacturing system. However, in the face of a large-scale environment, the existing methods have the problems of slow convergence and instability. To solve the problems above, we propose an improved optimization method, named improved-TC. Specifically, we are inspired by the horizontal crossover of CSO in the hybrid-TC teaching phase, the Hybrid-TC is proposed in our previous work, which is a hybrid of the teaching-learning-based optimization algorithm (TLBO) and the crisscross optimization algorithm (CSO). Improved-TC is an improvement on the learning phase of hybrid-TC algorithm, we change the search method of hybrid-TC in the learning phase to a one-dimensional search, thereby some dimensions in the population that are trapped in the local optimum have the chance to jump out of the iteration. Experiments show that our proposed method has a faster convergence speed and more stability in the face of service composition in large-scale environments.