An Effective Dynamic Web Service Selection Strategy with Global Optimal QoS Based on Particle Swarm Optimization Algorithm

Abstract

Dynamic Web service selection with global QoS optimization in Web service composition is a critical issue in Web service composition. In order to solve the problem, based on intelligent optimal theory of particle swarm optimization (PSO) algorithm, we propose a strategy PS O-GODSS (global optimization of dynamic Web service selection based on PSO) algorithm to implement Web service selection with QoS global optimization. The basic idea of the algorithm is to transform the original Web service selection problem into a multi-objective services composition optimization problem with global QoS constraints, which is further transformed into a single-objective problem by using the method of ideal point. Then, the theory of intelligent optimization of PS O is applied to produce a set of optimal services composition process with QoS constraints. Theoretical analysis and experimental results indicate the feasibility and efficiency of this algorithm, and the execution efficiency and convergence rate of PSO-GODSS are much better than that of multi-objective genetic algorithm used in prior work.

Utilizing Linked Open Data for Web Service Selection and Composition to Support e-Commerce Transactions

“…A number of researchers have utilized the PSO algorithm to adhere to similar problems in a variety of domain specific scenarios. The application of PSO for solving the WS selection and composition problem, based on QoS characteristics, is feasible as shown in [8] [17]. In this work we apply this algorithm as a part of the selection process, nonetheless other selection algorithms can also be applied, such as multicriteria decision analysis methods [16] or constraint algorithms [4].…”

Data-Intensive Service Provision Based on Particle Swarm Optimization

“…Zhang 15 proposed a QoS-aware Web service selection approach based on particle swarm optimization. Kang and so on 16 transformed the Web service selection problem into a multi-objective optimization problem with global QoS constraints, then introduced a particle swarm optimization algorithm to solve it. Ref.…”

Service Selection Method based on Skyline in Cloud Environment

“…We call it the only dominant area of service 2 s . However, the services on the upper right two sides of the rectangle do not belong to the only dominant area of 2 s , since they are dominated by service 1 s and service 3 s , respectively. If the failure service s is the dominated service, there is no effect on Skyline services.…”

“…Achieving the implementation of optimal service selection can reduce the cost of service selection. In [3], the authors proposed a dynamic service selection method based on the global QoS constraint decomposition. The method is based on adaptive adjustment of fuzzy logic and the adaptive particle swarm optimization algorithm.…”