In order to solve the problem of premature convergence caused by the decrease of population diversity in the optimization process of Krill Herd (KH) algorithm, an improved krill herd algorithm based on Generalized Opposition-Based Learning was proposed, namely GOBL-KH. Firstly, step size factors were determined by cosine decreasing strategy to balance the exploration and exploitation ability of the algorithm. Then, a generalized opposition-based learning strategy was added to search each krill, which enhanced the ability of the krill to explore the neighborhood space around it. The proposed algorithm was tested on fifteen benchmark functions and compared with the original KH algorithm, KH with Linear Decreasing step (KHLD) and KH with Cosiner Decreasing step (KHCD). The experimental results show that the proposed algorithm can effectively avoid premature and has higher accuracy. In order to demonstrate the effectiveness of the proposed algorithm, it was combined with K-means algorithm to solve the data clustering problem, namely HK-KH. In this fusion algorithm, after each iteration, the worst individual was replaced by the optimal individual or a new individual after the K-means iteration. Five datasets of UCI were used to test HK-KH algorithm and the results were compared with the K-means, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), KH, KH Clustering Algorithm (KHCA), Improved KH (IKH) algorithm for clustering problems. The experimental results show that HK-KH algorithm is suitable to solve the data clustering problem and has strong global convergence and high stability.

Concerning the present situation that Quality of Service (QoS) evaluation methods ignore the implicit service quality assessment and lead to inaccurate results, a service evaluation method that comprehensively considered explicit and implicit quality attributes was put forward. Explicit quality attributes were expressed in vector form, using service quality assessment model, after quantization, normalization, then evaluation values were calculated; and implicit quality attributes were expressed according to the evaluation on similar users' recommendation. The users' credibility and difference between old and new users were considered in the evaluation process. Finally the explicit and implicit quality evaluation was regarded as the QoS evaluation results. The experiments were performed in comparison with three algorithms by using one million Web Service QoS data. The simulation results show that the proposed method has certain feasibility and accuracy.