Multi-Strategy Improvement of Sparrow Search Algorithm for Cloud Manufacturing Service Composition

In existing research,the optimization of algorithms applied to cloud manufacturing service composition based on the quality of service often suffers from decreased convergence rates and solution quality due to single-population searches in fixed spaces and insufficient information exchange.In this paper,we introduce an improved Sparrow Search Algorithm(ISSA)to address these issues.The fixed solution space is divided into multiple subspaces,allowing for parallel searches that expedite the discovery of target solutions.To enhance search efficiency within these subspaces and significantly improve population diversity,we employ multiple group evolution mechanisms and chaotic perturbation strategies.Furthermore,we incorporate adaptive weights and a global capture strategy based on the golden sine to guide individual discoverers more effectively.Finally,differential Cauchy mutation perturbation is utilized during sparrow position updates to strengthen the algorithm's global optimization capabilities.Simulation experiments on benchmark problems and service composition optimization problems show that the ISSA delivers superior optimization accuracy and convergence stability compared to other methods.These results demonstrate that our approach effectively balances global and local search abilities,leading to enhanced performance in cloud manufacturing service composition.

A Lambda Layer-Based Convolutional Sequence Embedding Model for Click-Through Rate Prediction

In the era of intelligent economy, the click-through rate(CTR) prediction system can evaluate massive service information based on user historical information, and screen out the products that are most likely to be favored by users, thus realizing customized push of information and achieve the ultimate goal of improving economic benefits. Sequence modeling is one of the main research directions of CTR prediction models based on deep learning. The user's general interest hidden in the entire click history and the short-term interest hidden in the recent click behaviors have different influences on the CTR prediction results, which are highly important. In terms of capturing the user's general interest, existing models paid more attention to the relationships between item embedding vectors(point-level), while ignoring the relationships between elements in item embedding vectors(union-level). The Lambda layer-based Convolutional Sequence Embedding(LCSE) model proposed in this paper uses the Lambda layer to capture features from click history through weight distribution, and uses horizontal and vertical filters on this basis to learn the user's general preferences from union-level and point-level. In addition, we also incorporate the user's short-term preferences captured by the embedding-based convolutional model to further improve the prediction results. The AUC(Area Under Curve) values of the LCSE model on the datasets Electronic, Movie & TV and MovieLens are 0.870 7, 0.903 6 and 0.946 7, improving 0.45%, 0.36% and 0.07% over the Caser model, proving the effectiveness of our proposed model.

IDL: Evaluating software quality based on PageRank algorithm

In the traditional method, the software quality is measured by various metrics of the software, such as decoupling level(DL), which can be used to predict software defect. However, DL, which treats all the files equally, has not taken file importance into consideration. Therefore, a novel software quality metric, named as improved decoupling level(IDL), based on the importance of documents was proposed. First, the PageRank algorithm was used to calculate the importance of files to obtain the weights of the dependencies, and then defect prediction models was established by combining the software scale, dependencies, scores and software defects to assess the software quality. Compared to most existing module-based software quality evaluation methods, IDL has similar or even superior performance in the prediction of software quality. The results indicate that IDL measures the importance of each file in the software more accurately by combining the PageRank algorithm in DL, which indirectly reflects the quality of software by predicting the bug information in software and improves the accuracy of prediction result of software bug information.