Web Service Composition Optimization Based on Adaptive Mutant Beetle Swarm

Abstract with the continuous development of cloud computing technology and big data, web services are widely used in intelligent information processing in all walks of life, when facing a large number of services with similar functions and quality, web service composition technology is a crucial technology in web services, which enable common services to be combined and select the optimal service combination to meet the wide range of users’ needs. Aiming at the problems of algorithm accuracy and stability of existing service composition technologies, the penalty function is used to construct the fitness function in this paper, which transforms the constrained optimization problem into an unconstrained optimization problem, and realizes the global optimization. Traditional web service composition models are aimed at QoS attribute perception. This paper establishes a QoE model that focuses on user experience, which can better meet the needs of users. An adaptive mutant beetle herd algorithm was proposed in this paper, the author introduced Beetle Antennae Searching Algorithm into the common particle swarm optimization algorithm, and added an adaptive mutation factor to enhance the searching ability of the algorithm; The use of dynamic learning factors allows the algorithm to have better population diversity and enhanced convergence. The experimental results that the algorithm proposed in this paper has higher algorithm accuracy, comparatively faster convergence speed and stability.